upd agent; add analys task

ai_interviewer_agent.py

@@ -3,7 +3,7 @@ import asyncio
 import json
 import logging
 import os
-from typing import Dict, List
+from typing import Dict, List, Optional
 from datetime import datetime
 
 # Принудительно устанавливаем UTF-8 для Windows
@@ -12,26 +12,51 @@ if os.name == 'nt':  # Windows
     if hasattr(sys, 'stdout') and hasattr(sys.stdout, 'reconfigure'):
         sys.stdout.reconfigure(encoding='utf-8', errors='replace')
         sys.stderr.reconfigure(encoding='utf-8', errors='replace')
+    
+    # Устанавливаем переменную окружения для Python
+    os.environ.setdefault('PYTHONIOENCODING', 'utf-8')
+
 from livekit.agents import (
     Agent,
     AgentSession,
     JobContext,
     WorkerOptions,
     cli,
+    NotGiven
 )
-from livekit.plugins import openai, deepgram, cartesia, silero, resemble
+from livekit.plugins import openai, deepgram, cartesia, silero
+from livekit.api import LiveKitAPI, DeleteRoomRequest
 from rag.settings import settings
+from app.core.database import get_session
+from app.repositories.interview_repository import InterviewRepository
+from app.repositories.resume_repository import ResumeRepository
+from app.services.interview_finalization_service import InterviewFinalizationService
 
 logger = logging.getLogger("ai-interviewer")
 logger.setLevel(logging.INFO)
 
+
+async def close_room(room_name: str):
+    """Закрывает LiveKit комнату полностью (отключает всех участников)"""
+    try:
+        api = LiveKitAPI(settings.livekit_url, settings.livekit_api_key, settings.livekit_api_secret)
+        # Создаем RoomService для управления комнатами
+        await api.room.delete_room(delete=DeleteRoomRequest(room=room_name))
+
+        logger.info(f"[ROOM_MANAGEMENT] Room {room_name} deleted successfully")
+        
+    except Exception as e:
+        logger.error(f"[ROOM_MANAGEMENT] Failed to delete room {room_name}: {str(e)}")
+        raise
+
+
 class InterviewAgent:
     """AI Agent для проведения собеседований с управлением диалогом"""
-    
+
     def __init__(self, interview_plan: Dict):
         self.interview_plan = interview_plan
         self.conversation_history = []
-        
+
         # Состояние диалога
         self.current_section = 0
         self.current_question_in_section = 0
@@ -39,378 +64,416 @@ class InterviewAgent:
         self.waiting_for_response = False
         self.last_question = None
         self.last_user_response = None
+        self.intro_done = False  # Новый флаг — произнесено ли приветствие
+        self.interview_finalized = False  # Флаг завершения интервью
+        
+        # Трекинг времени интервью
+        import time
+        self.interview_start_time = time.time()
+        self.duration_minutes = interview_plan.get('interview_structure', {}).get('duration_minutes', 10)
         
-        # Извлекаем структуру интервью
         self.sections = self.interview_plan.get('interview_structure', {}).get('sections', [])
         self.total_sections = len(self.sections)
-    
+        
+        logger.info(f"[TIME] Interview started at {time.strftime('%H:%M:%S')}, duration: {self.duration_minutes} min")
+
     def get_current_section(self) -> Dict:
         """Получить текущую секцию интервью"""
         if self.current_section < len(self.sections):
             return self.sections[self.current_section]
         return {}
-    
+
     def get_next_question(self) -> str:
-        """Получить следующий вопрос из текущей секции"""
+        """Получить следующий вопрос"""
         section = self.get_current_section()
         questions = section.get('questions', [])
-        
         if self.current_question_in_section < len(questions):
             return questions[self.current_question_in_section]
         return None
-    
+
     def move_to_next_question(self):
         """Переход к следующему вопросу"""
-        section = self.get_current_section()
-        questions = section.get('questions', [])
-        
         self.current_question_in_section += 1
         self.questions_asked_total += 1
-        
-        # Если вопросы в секции закончились, переходим к следующей
-        if self.current_question_in_section >= len(questions):
+
+        section = self.get_current_section()
+        if self.current_question_in_section >= len(section.get('questions', [])):
             self.move_to_next_section()
-    
+
     def move_to_next_section(self):
         """Переход к следующей секции"""
         self.current_section += 1
         self.current_question_in_section = 0
-        
         if self.current_section < len(self.sections):
             logger.info(f"Переход к секции: {self.sections[self.current_section].get('name', 'Unnamed')}")
-    
+
     def is_interview_complete(self) -> bool:
-        """Проверяет, завершено ли интервью"""
-        return self.current_section >= len(self.sections)
-    
-    async def analyze_user_response(self, response: str, chat_model) -> Dict[str, str]:
-        """Анализирует ответ пользователя и решает следующий шаг"""
-        try:
-            from rag.registry import registry
-            
-            analysis_prompt = f"""
-            Проанализируй ответ кандидата на интервью и определи следующий шаг.
-            
-            КОНТЕКСТ:
-            - Текущая секция: {self.get_current_section().get('name', 'Unknown')}
-            - Заданный вопрос: {self.last_question}
-            - Ответ кандидата: {response}
-            
-            Оцени ответ и определи действие:
-            1. "continue" - ответ полный, переходим к следующему вопросу
-            2. "clarify" - нужно уточнить или углубить ответ
-            3. "redirect" - нужно перенаправить на тему
-            
-            Ответь в JSON формате:
-            {{
-                "action": "continue|clarify|redirect",
-                "reason": "Объяснение решения",
-                "follow_up_question": "Уточняющий вопрос если action=clarify или redirect"
-            }}
-            """
-            
-            from langchain.schema import HumanMessage, SystemMessage
-            messages = [
-                SystemMessage(content="Ты эксперт-аналитик интервью. Анализируй ответы объективно."),
-                HumanMessage(content=analysis_prompt)
-            ]
-            
-            response_analysis = chat_model.chat(messages)
-            response_text = response_analysis.content.strip()
-            
-            # Парсим JSON ответ
-            if response_text.startswith('{') and response_text.endswith('}'):
-                return json.loads(response_text)
-            else:
-                # Fallback
-                return {
-                    "action": "continue",
-                    "reason": "Не удалось проанализировать ответ",
-                    "follow_up_question": ""
-                }
-                
-        except Exception as e:
-            logger.error(f"Ошибка анализа ответа: {str(e)}")
-            return {
-                "action": "continue",
-                "reason": "Ошибка анализа",
-                "follow_up_question": ""
-            }
-    
-    def _extract_questions_from_plan(self) -> List[str]:
-        """Извлечение вопросов из готового плана интервью"""
-        questions = []
-        
-        try:
-            # Начинаем с приветствия из плана
-            greeting = self.interview_plan.get('interview_structure', {}).get('greeting', 'Привет! Готов к интервью?')
-            questions.append(greeting)
-            
-            # Извлекаем вопросы из секций
-            sections = self.interview_plan.get('interview_structure', {}).get('sections', [])
-            
-            for section in sections:
-                section_questions = section.get('questions', [])
-                questions.extend(section_questions)
-            
-            return questions
-            
-        except Exception as e:
-            logger.error(f"Ошибка извлечения вопросов из плана: {str(e)}")
-            # Fallback вопросы
-            return [
-                "Привет! Расскажи немного о себе",
-                "Какой у тебя опыт работы?",
-                "Что тебя привлекает в этой позиции?",
-                "Есть ли у тебя вопросы ко мне?"
-            ]
-    
+        """Интервью завершается только по решению LLM через ключевые фразы"""
+        return False  # LLM теперь решает через ключевые фразы
+
     def get_system_instructions(self) -> str:
-        """Системные инструкции для AI агента"""
+        """Системные инструкции для AI агента с ключевыми фразами для завершения"""
         candidate_info = self.interview_plan.get('candidate_info', {})
         interview_structure = self.interview_plan.get('interview_structure', {})
-        focus_areas = self.interview_plan.get('focus_areas', [])
-        
         greeting = interview_structure.get('greeting', 'Привет! Готов к интервью?')
+        focus_areas = self.interview_plan.get('focus_areas', [])
+        key_evaluation_points = self.interview_plan.get('key_evaluation_points', [])
         
-        current_section = self.get_current_section()
-        current_section_name = current_section.get('name', 'Неизвестно')
-        progress = f"{self.current_section + 1}/{len(self.sections)}"
+        # Вычисляем текущее время интервью
+        import time
+        elapsed_minutes = (time.time() - self.interview_start_time) / 60
+        remaining_minutes = max(0, self.duration_minutes - elapsed_minutes)
+        time_percentage = min(100, (elapsed_minutes / self.duration_minutes) * 100)
         
-        return f"""Ты опытный HR-интервьюер, который проводит структурированное голосовое собеседование.
+        # Формируем план интервью для агента
+        sections_info = "\n".join([
+            f"- {section.get('name', 'Секция')}: {', '.join(section.get('questions', []))}"
+            for section in self.sections
+        ])
+
+        # Безопасно формируем строки для избежания конфликтов с кавычками
+        candidate_name = candidate_info.get('name', 'Кандидат')
+        candidate_years = candidate_info.get('total_years', 0)
+        candidate_skills = ', '.join(candidate_info.get('skills', []))
+        focus_areas_str = ', '.join(focus_areas) 
+        evaluation_points_str = ', '.join(key_evaluation_points)
+        
+        # Статус времени
+        if time_percentage > 90:
+            time_status = 'СРОЧНО ЗАВЕРШАТЬ'
+        elif time_percentage > 75:
+            time_status = 'ВРЕМЯ ЗАКАНЧИВАЕТСЯ'
+        else:
+            time_status = 'НОРМАЛЬНО'
+        
+        return f"""Ты опытный HR-интервьюер, который проводит адаптивное голосовое собеседование.
 
 ИНФОРМАЦИЯ О КАНДИДАТЕ:
-- Имя: {candidate_info.get('name', 'Кандидат')}
-- Опыт работы: {candidate_info.get('total_years', 0)} лет
-- Ключевые навыки: {', '.join(candidate_info.get('skills', []))}
+- Имя: {candidate_name}
+- Опыт работы: {candidate_years} лет
+- Ключевые навыки: {candidate_skills}
 
-ТЕКУЩЕЕ СОСТОЯНИЕ ИНТЕРВЬЮ:
-- Прогресс: {progress} секций
-- Текущая секция: {current_section_name}
-- Вопросов задано: {self.questions_asked_total}
+ПЛАН ИНТЕРВЬЮ (используй как руководство, но адаптируйся):
+{sections_info}
 
-ПЛАН ИНТЕРВЬЮ:
-{json.dumps(interview_structure.get('sections', []), ensure_ascii=False, indent=2)}
+ВРЕМЯ ИНТЕРВЬЮ:
+- Запланированная длительность: {self.duration_minutes} минут
+- Прошло времени: {elapsed_minutes:.1f} минут ({time_percentage:.0f}%)
+- Осталось времени: {remaining_minutes:.1f} минут
+- Статус времени: {time_status}
 
-ТВОЯ ЗАДАЧА:
-1. Веди живое интерактивное интервью
-2. Анализируй каждый ответ кандидата
-3. Принимай решения:
-   - Если ответ полный и достаточный → переходи к следующему вопросу
-   - Если ответ поверхностный → задавай уточняющие вопросы
-   - Если кандидат ушел от темы → мягко возвращай к вопросу
-4. Поддерживай естественный диалог
+ФОКУС-ОБЛАСТИ: {focus_areas_str}
+КЛЮЧЕВЫЕ ОЦЕНОЧНЫЕ ТОЧКИ: {evaluation_points_str}
 
-ПРАВИЛА ВЕДЕНИЯ ДИАЛОГА:
-✅ Говори только на русском языке
-✅ Задавай один вопрос за раз и жди ответа
-✅ Анализируй качество и полноту каждого ответа
-✅ Адаптируй следующие вопросы под полученные ответы
-✅ Показывай искреннюю заинтересованность
-✅ Если ответ неполный - углубляйся: "Расскажи подробнее...", "А как именно ты..."
-✅ При переходе между секциями делай плавные переходы
-✅ Завершай интервью благодарностью и следующими шагами
+ИНСТРУКЦИИ:
+1. Начни с приветствия: {greeting}
+2. Адаптируй вопросы под ответы кандидата
+3. Следи за временем - при превышении 80% времени начинай завершать интервью
+4. Оценивай качество и глубину ответов кандидата
+5. Завершай интервью если:
+   - Получил достаточно информации для оценки
+   - Время почти истекло (>90% от запланированного)
+   - Кандидат дал исчерпывающие ответы
+6. При завершении спроси о вопросах кандидата и поблагодари
 
-ПРИМЕРЫ РЕАКЦИЙ НА ОТВЕТЫ:
-- Короткий ответ: "Интересно! А можешь рассказать конкретный пример?"
-- Хороший ответ: "Отлично! Давай перейдем к следующему вопросу..."
-- Уход от темы: "Понимаю, но давай вернемся к..."
+ВАЖНО: Отвечай естественно и разговорно, как живой интервьюер!
 
-НАЧНИ С ПРИВЕТСТВИЯ: {greeting}
+ЗАВЕРШЕНИЕ ИНТЕРВЬЮ:
+Когда нужно завершить интервью (время истекло, получена достаточная информация), 
+используй одну из этих ключевых фраз:
+- "Спасибо за интересную беседу! У тебя есть вопросы ко мне?"
+- "Это всё, что я хотел узнать. Есть ли у вас вопросы?"  
+- "Интервью подходит к концу. У тебя есть вопросы ко мне?"
+ФИНАЛЬНАЯ ФРАЗА после которой ничего не будет:
+- До скорой встречи!
+
+ЗАВЕРШАЙ ИНТЕРВЬЮ, если:
+- Прошло >80% времени И получил основную информацию
+- Кандидат дал полные ответы по всем ключевым областям
+- Возникли технические проблемы или кандидат просит завершить
+
+СТИЛЬ: Дружелюбный, профессиональный, заинтересованный в кандидате.
 """
 
+    def get_time_info(self) -> Dict[str, float]:
+        """Получает информацию о времени интервью"""
+        import time
+        elapsed_minutes = (time.time() - self.interview_start_time) / 60
+        remaining_minutes = max(0.0, self.duration_minutes - elapsed_minutes)
+        time_percentage = min(100.0, (elapsed_minutes / self.duration_minutes) * 100)
+        
+        return {
+            "elapsed_minutes": elapsed_minutes,
+            "remaining_minutes": remaining_minutes, 
+            "time_percentage": time_percentage,
+            "duration_minutes": self.duration_minutes
+        }
+    
+    async def track_interview_progress(self, user_response: str) -> Dict[str, any]:
+        """Трекает прогресс интервью для логирования"""
+        current_section = self.get_current_section()
+        time_info = self.get_time_info()
+        
+        return {
+            "section": current_section.get('name', 'Unknown'),
+            "questions_asked": self.questions_asked_total,
+            "section_progress": f"{self.current_section + 1}/{len(self.sections)}",
+            "user_response_length": len(user_response),
+            "elapsed_minutes": f"{time_info['elapsed_minutes']:.1f}",
+            "remaining_minutes": f"{time_info['remaining_minutes']:.1f}",
+            "time_percentage": f"{time_info['time_percentage']:.0f}%"
+        }
+
 
 async def entrypoint(ctx: JobContext):
     """Точка входа для AI агента"""
-    logger.info("Starting AI Interviewer Agent")
+    logger.info("[INIT] Starting AI Interviewer Agent")
+    logger.info(f"[INIT] Room: {ctx.room.name}")
+
+    # План интервью - получаем из переменной окружения
+    room_metadata = os.environ.get("LIVEKIT_ROOM_METADATA", ctx.room.metadata or "{}")
     
-    # Получаем данные о резюме из метаданных комнаты
-    room_metadata = ctx.room.metadata if ctx.room.metadata else "{}"
     try:
         metadata = json.loads(room_metadata)
         interview_plan = metadata.get("interview_plan", {})
-        if not hasattr(interview_plan, 'interview_structure'):
-            raise ValueError
-    except:
-        # Fallback план для тестирования
+        if not interview_plan:
+            interview_plan = {}
+    except Exception as e:
+        logger.warning(f"[INIT] Failed to parse metadata: {str(e)}")
+        interview_plan = {}
+    
+    # Используем дефолтный план если план пустой или нет секций
+    if not interview_plan or not interview_plan.get("interview_structure", {}).get("sections"):
+        logger.info(f"[INIT] Using default interview plan")
         interview_plan = {
             "interview_structure": {
-                "duration_minutes": 30,
-                "greeting": "Привет! Готов к тестовому интервью?",
+                "duration_minutes": 2,  # ТЕСТОВЫЙ РЕЖИМ - 2 минуты
+                "greeting": "Привет! Это быстрое тестовое интервью на 2 минуты. Готов?",
                 "sections": [
-                    {
-                        "name": "Знакомство",
-                        "duration_minutes": 10,
-                        "questions": ["Расскажи о себе", "Что тебя привлекло в этой позиции?"]
-                    },
-                    {
-                        "name": "Технические навыки",
-                        "duration_minutes": 15,
-                        "questions": ["Расскажи о своем опыте с Python", "Какие проекты разрабатывал?"]
-                    },
-                    {
-                        "name": "Вопросы кандидата",
-                        "duration_minutes": 5,
-                        "questions": ["Есть ли у тебя вопросы ко мне?"]
-                    }
+                    {"name": "Знакомство", "duration_minutes": 1, "questions": ["Расскажи кратко о себе одним предложением"]},
+                    {"name": "Завершение", "duration_minutes": 1, "questions": ["Спасибо! Есть вопросы ко мне?"]}
                 ]
             },
-            "focus_areas": ["technical_skills", "experience"],
-            "candidate_info": {
-                "name": "Тестовый кандидат",
-                "skills": ["Python", "React", "PostgreSQL"],
-                "total_years": 3,
-                "education": "Высшее техническое"
-            }
+            "candidate_info": {"name": "Тестовый кандидат", "skills": ["Python", "React"], "total_years": 3},
+            "focus_areas": ["quick_test"],
+            "key_evaluation_points": ["Коммуникация"]
         }
-    
-    logger.info(f"Interview plan: {interview_plan}")
-    
-    # Создаем интервьюера с планом
+
     interviewer = InterviewAgent(interview_plan)
-    
-    # Настройка STT (Speech-to-Text)
-    if hasattr(settings, 'deepgram_api_key') and settings.deepgram_api_key:
-        stt = deepgram.STT(
-            model="nova-2-general",
-            language="ru",  # Русский язык
-            api_key=settings.deepgram_api_key
-        )
-    else:
-        # Fallback на OpenAI Whisper
-        stt = openai.STT(
-            model="whisper-1",
-            language="ru",
-            api_key=settings.openai_api_key
-        )
-    
-    # Настройка LLM
-    llm = openai.LLM(
-        model="gpt-4o-mini",
-        api_key=settings.openai_api_key,
-        temperature=0.7,
-    )
-    
-    # Настройка TTS (Text-to-Speech)  
-    if hasattr(settings, 'resemble_api_key') and settings.resemble_api_key:
-        tts = resemble.TTS(
-            voice_uuid="55592656",
-            api_key=settings.resemble_api_key
-        )
-    else:
-        # Fallback на локальный TTS
-        tts = silero.TTS(
-            language="ru",
-            model="v4_ru"
-        )
-    
-    # Создание агента с системными инструкциями
-    agent = Agent(
-        instructions=interviewer.get_system_instructions()
-    )
-    
-    # Создание сессии агента
-    session = AgentSession(
-        vad=silero.VAD.load(),  # Voice Activity Detection
-        stt=stt,
-        llm=llm,
-        tts=tts,
-    )
-    
-    # Добавляем обработчики событий с управлением диалогом
-    @session.on("user_speech_committed")
-    def on_user_speech(msg):
-        """Синхронный callback. Внутри создаётся async-задача."""
+    logger.info(f"[INIT] InterviewAgent created with {len(interviewer.sections)} sections")
 
-        async def handler():
-            user_response = msg.content
-            logger.info(f"User said: {user_response}")
+    # STT
+    stt = deepgram.STT(model="nova-2-general", language="ru", api_key=settings.deepgram_api_key) \
+        if settings.deepgram_api_key else openai.STT(model="whisper-1", language="ru", api_key=settings.openai_api_key)
 
-            # Сохраняем историю
-            interviewer.conversation_history.append({
-                "role": "user",
-                "content": user_response,
-                "timestamp": datetime.utcnow().isoformat(),
-                "section": interviewer.get_current_section().get('name', 'Unknown')
-            })
+    # LLM
+    llm = openai.LLM(model="gpt-4o-mini", api_key=settings.openai_api_key, temperature=0.7)
 
-            interviewer.last_user_response = user_response
-            interviewer.waiting_for_response = False
+    # TTS
+    tts = cartesia.TTS(model="sonic-turbo", language="ru", voice='da05e96d-ca10-4220-9042-d8acef654fa9',
+                       api_key=settings.cartesia_api_key) if settings.cartesia_api_key else silero.TTS(language="ru", model="v4_ru")
 
+    # Создаем обычный Agent и Session
+    agent = Agent(instructions=interviewer.get_system_instructions())
+    
+    # Создаем AgentSession с обычным TTS
+    session = AgentSession(vad=silero.VAD.load(), stt=stt, llm=llm, tts=tts)
+
+    # --- Сохранение диалога в БД ---
+    async def save_dialogue_to_db(room_name: str, dialogue_history: list):
+        try:
+            session_generator = get_session()
+            db = await anext(session_generator)
             try:
-                # Анализ ответа
-                analysis = await interviewer.analyze_user_response(user_response, llm)
-                action = analysis.get("action", "continue")
+                interview_repo = InterviewRepository(db)
+                resume_repo = ResumeRepository(db)
+                finalization_service = InterviewFinalizationService(interview_repo, resume_repo)
+                success = await finalization_service.save_dialogue_to_session(room_name, dialogue_history)
+                if not success:
+                    logger.warning(f"[DB] Failed to save dialogue for room: {room_name}")
+            finally:
+                await session_generator.aclose()
+        except Exception as e:
+            logger.error(f"[DB] Error saving dialogue: {str(e)}")
 
-                logger.info(f"Response analysis: {action} - {analysis.get('reason', 'No reason')}")
+    # --- Логика завершения интервью ---
+    async def finalize_interview(room_name: str, interviewer_instance):
+        """Завершение интервью и запуск анализа"""
+        
+        # Проверяем, не завершено ли уже интервью
+        if interviewer_instance.interview_finalized:
+            logger.info(f"[FINALIZE] Interview already finalized for room: {room_name}")
+            return
+            
+        interviewer_instance.interview_finalized = True
+        
+        try:
+            logger.info(f"[FINALIZE] Starting interview finalization for room: {room_name}")
+            
+            # Собираем метрики интервью
+            time_info = interviewer_instance.get_time_info()
+            interview_metrics = {
+                "total_messages": interviewer_instance.questions_asked_total,
+                "dialogue_length": len(interviewer_instance.conversation_history),
+                "elapsed_minutes": time_info['elapsed_minutes'],
+                "planned_duration": time_info['duration_minutes'],
+                "time_percentage": time_info['time_percentage']
+            }
+            
+            session_generator = get_session()
+            db = await anext(session_generator)
+            try:
+                interview_repo = InterviewRepository(db)
+                resume_repo = ResumeRepository(db)
+                finalization_service = InterviewFinalizationService(interview_repo, resume_repo)
+                
+                # Используем сервис для завершения интервью
+                result = await finalization_service.finalize_interview(
+                    room_name=room_name,
+                    dialogue_history=interviewer_instance.conversation_history,
+                    interview_metrics=interview_metrics
+                )
+                
+                if result:
+                    logger.info(f"[FINALIZE] Interview successfully finalized: session_id={result['session_id']}, task_id={result['analysis_task_id']}")
 
-                if action == "continue":
-                    interviewer.move_to_next_question()
-
-                    if not interviewer.is_interview_complete():
-                        next_question = interviewer.get_next_question()
-                        if next_question:
-                            await session.say(next_question)
-                            interviewer.last_question = next_question
-                            interviewer.waiting_for_response = True
-                    else:
-                        await session.say(
-                            "Спасибо за интервью! Это все вопросы, которые я хотел задать. "
-                            "В ближайшее время мы свяжемся с тобой по результатам."
-                        )
-
-                elif action in ["clarify", "redirect"]:
-                    follow_up = analysis.get("follow_up_question", "Можешь рассказать подробнее?")
-                    await session.say(follow_up)
-                    interviewer.waiting_for_response = True
+                else:
+                    logger.error(f"[FINALIZE] Failed to finalize interview for room: {room_name}")
+            finally:
+                await session_generator.aclose()
+        except Exception as e:
+            logger.error(f"[FINALIZE] Error finalizing interview: {str(e)}")
 
+    # --- Проверка завершения интервью по ключевым фразам ---
+    async def check_interview_completion_by_keywords(agent_text: str):
+        """Проверяет завершение интервью по ключевым фразам"""
+        # Ключевые фразы для завершения интервью
+        ending_keywords = [
+            "До скорой встречи"
+        ]
+        
+        text_lower = agent_text.lower()
+        
+        for keyword in ending_keywords:
+            if keyword.lower() in text_lower:
+                logger.info(f"[KEYWORD_DETECTION] Found ending keyword: '{keyword}' in agent response")
+                
+                if not interviewer.interview_finalized:
+                    # Запускаем полную цепочку завершения интервью
+                    await complete_interview_sequence(ctx.room.name, interviewer)
+                    return True
+                break
+        
+        return False
+    
+    # --- Полная цепочка завершения интервью ---
+    async def complete_interview_sequence(room_name: str, interviewer_instance):
+        """
+        Полная цепочка завершения интервью:
+        1. Финализация диалога в БД
+        2. Закрытие комнаты LiveKit
+        3. Завершение процесса агента
+        """
+        try:
+            logger.info("[SEQUENCE] Starting interview completion sequence")
+            
+            # Шаг 1: Финализируем интервью в БД
+            logger.info("[SEQUENCE] Step 1: Finalizing interview in database")
+            await finalize_interview(room_name, interviewer_instance)
+            logger.info("[SEQUENCE] Step 1: Database finalization completed")
+            
+            # Даём время на завершение всех DB операций
+            await asyncio.sleep(1)
+            
+            # Шаг 2: Закрываем комнату LiveKit
+            logger.info("[SEQUENCE] Step 2: Closing LiveKit room")
+            try:
+                await close_room(room_name)
+                logger.info(f"[SEQUENCE] Step 2: Room {room_name} closed successfully")
             except Exception as e:
-                logger.error(f"Ошибка обработки ответа пользователя: {str(e)}")
-                interviewer.move_to_next_question()
+                logger.error(f"[SEQUENCE] Step 2: Failed to close room: {str(e)}")
+                logger.info("[SEQUENCE] Step 2: Room closure failed, but continuing sequence")
+            
+            # Шаг 3: Завершаем процесс агента
+            logger.info("[SEQUENCE] Step 3: Terminating agent process")
+            await asyncio.sleep(2)  # Даём время на завершение всех операций
+            logger.info("[SEQUENCE] Step 3: Force terminating agent process")
+            import os
+            os._exit(0)  # Принудительное завершение процесса
+            
+        except Exception as e:
+            logger.error(f"[SEQUENCE] Error in interview completion sequence: {str(e)}")
+            # Fallback: принудительно завершаем процесс даже при ошибках
+            logger.info("[SEQUENCE] Fallback: Force terminating process")
+            await asyncio.sleep(1)
+            import os
+            os._exit(1)
 
-        # запускаем асинхронный обработчик
-        asyncio.create_task(handler())
-    
-    @session.on("agent_speech_committed") 
-    def on_agent_speech(msg):
-        """Обработка речи агента"""
-        agent_response = msg.content
-        logger.info(f"Agent said: {agent_response}")
+
+    # --- Упрощенная логика обработки пользовательского ответа ---
+    async def handle_user_input(user_response: str):
+        current_section = interviewer.get_current_section()
         
-        # Сохраняем в историю
-        interviewer.conversation_history.append({
-            "role": "assistant",
-            "content": agent_response, 
+        # Сохраняем ответ пользователя
+        dialogue_message = {
+            "role": "user",
+            "content": str(user_response).encode('utf-8').decode('utf-8'),  # Принудительное UTF-8
             "timestamp": datetime.utcnow().isoformat(),
-            "section": interviewer.get_current_section().get('name', 'Unknown')
-        })
+            "section": current_section.get('name', 'Unknown')
+        }
+        interviewer.conversation_history.append(dialogue_message)
+        await save_dialogue_to_db(ctx.room.name, interviewer.conversation_history)
         
-        # Если это вопрос, обновляем состояние
-        if "?" in agent_response:
-            interviewer.last_question = agent_response
-            interviewer.waiting_for_response = True
-    
-    # Запускаем сессию агента
+        # Обновляем прогресс интервью
+        if not interviewer.intro_done:
+            interviewer.intro_done = True
+        
+        # Обновляем счетчик сообщений и треким время
+        interviewer.questions_asked_total += 1
+        progress_info = await interviewer.track_interview_progress(user_response)
+        logger.info(f"[PROGRESS] Messages: {progress_info['questions_asked']}, Time: {progress_info['elapsed_minutes']}min/{progress_info['time_percentage']}")
+        
+        
+        # Обновляем инструкции агента с текущим прогрессом
+        try:
+            updated_instructions = interviewer.get_system_instructions()
+            await agent.update_instructions(updated_instructions)
+        except Exception as e:
+            logger.error(f"[ERROR] Failed to update instructions: {str(e)}")
+
+    @session.on("conversation_item_added")
+    def on_conversation_item(event):
+        role = event.item.role  
+        text = event.item.text_content
+
+        if role == "user":
+            asyncio.create_task(handle_user_input(text))
+        elif role == "assistant":
+            # Сохраняем ответ агента в историю диалога
+            current_section = interviewer.get_current_section()
+            interviewer.conversation_history.append({
+                "role": "assistant", 
+                "content": str(text).encode('utf-8').decode('utf-8'),  # Принудительное UTF-8
+                "timestamp": datetime.utcnow().isoformat(),
+                "section": current_section.get('name', 'Unknown')
+            })
+            
+            # Сохраняем диалог в БД
+            asyncio.create_task(save_dialogue_to_db(ctx.room.name, interviewer.conversation_history))
+            
+            # Проверяем ключевые фразы для завершения интервью
+            asyncio.create_task(check_interview_completion_by_keywords(text))
+
+
     await session.start(agent=agent, room=ctx.room)
-    
-    # Приветственное сообщение
-    # В новой версии приветствие будет автоматически отправлено из системных инструкций
-    
-    logger.info("AI Interviewer started successfully")
+    logger.info("[INIT] AI Interviewer started")
 
 
 def main():
-    """Запуск агента"""
     logging.basicConfig(level=logging.INFO)
-    
-    # Настройки воркера
-    worker_options = WorkerOptions(
-        entrypoint_fnc=entrypoint,
-    )
-    
-    # Запуск через CLI
-    cli.run_app(worker_options)
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())  # фикс для Windows
+    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))
 
 
 if __name__ == "__main__":
-    main()
+    main()

app/core/database.py

@@ -1,25 +1,60 @@
-from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine, async_sessionmaker
+from typing import AsyncGenerator, Generator
+from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
+from sqlalchemy.orm import sessionmaker, Session
+from sqlalchemy import create_engine
 from sqlmodel import SQLModel
+
 from .config import settings
 
-engine = create_async_engine(
+# Async engine для FastAPI
+async_engine = create_async_engine(
     settings.database_url,
     echo=settings.debug,
-    future=True
+    future=True,
+    pool_size=10,
+    max_overflow=20,
+    pool_timeout=30,
+    pool_pre_ping=True,
 )
 
-async_session_maker = async_sessionmaker(
-    bind=engine,
+async_session = sessionmaker(
+    async_engine,
+    expire_on_commit=False,
     class_=AsyncSession,
-    expire_on_commit=False
+)
+
+# Sync engine для Celery и других синхронных операций
+sync_engine = create_engine(
+    settings.database_url.replace("asyncpg", "psycopg2"),
+    echo=settings.debug,
+    future=True,
+    pool_size=10,
+    max_overflow=20,
+    pool_timeout=30,
+    pool_pre_ping=True,
+    pool_recycle=3600,
+)
+
+sync_session = sessionmaker(
+    bind=sync_engine,
+    expire_on_commit=False,
+    class_=Session,
 )
 
 
-async def get_session() -> AsyncSession:
-    async with async_session_maker() as session:
+async def get_session() -> AsyncGenerator[AsyncSession, None]:
+    """Async session для FastAPI DI"""
+    async with async_session() as session:
+        yield session
+
+
+def get_sync_session() -> Generator[Session, None, None]:
+    """Sync session для Celery и других синхронных операций"""
+    with sync_session() as session:
         yield session
 
 
 async def create_db_and_tables():
-    async with engine.begin() as conn:
-        await conn.run_sync(SQLModel.metadata.create_all)
+    """Создать таблицы в БД"""
+    async with async_engine.begin() as conn:
+        await conn.run_sync(SQLModel.metadata.create_all)

app/core/session_middleware.py

@@ -3,7 +3,7 @@ from fastapi.responses import JSONResponse
 from starlette.middleware.base import BaseHTTPMiddleware
 from starlette.types import ASGIApp
 from sqlalchemy.ext.asyncio import AsyncSession
-from app.core.database import async_session_maker
+from app.core.database import get_session
 from app.repositories.session_repository import SessionRepository
 from app.models.session import Session
 import logging
@@ -32,7 +32,7 @@ class SessionMiddleware(BaseHTTPMiddleware):
         
         try:
             # Работаем с БД в рамках одной async сессии
-            async with async_session_maker() as db_session:
+            async for db_session in get_session():
                 session_repo = SessionRepository(db_session)
                 
                 # Проверяем существующую сессию
@@ -56,6 +56,7 @@ class SessionMiddleware(BaseHTTPMiddleware):
 
                 # Добавляем сессию в контекст запроса
                 request.state.session = session_obj
+                break
 
         except Exception as e:
             logger.error(f"Session middleware error: {e}")
@@ -83,10 +84,4 @@ class SessionMiddleware(BaseHTTPMiddleware):
 
 async def get_current_session(request: Request) -> Session:
     """Получить текущую сессию из контекста запроса"""
-    return getattr(request.state, 'session', None)
-
-
-async def get_db_session() -> AsyncSession:
-    """Получить новую сессию БД для использования в эндпоинтах"""
-    async with async_session_maker() as session:
-        yield session
+    return getattr(request.state, 'session', None)

app/models/__init__.py

@@ -1,6 +1,21 @@
 from .vacancy import Vacancy, VacancyCreate, VacancyUpdate, VacancyRead
 from .resume import Resume, ResumeCreate, ResumeUpdate, ResumeRead
 from .session import Session, SessionCreate, SessionRead
+from .interview import (
+    InterviewSession, 
+    InterviewSessionCreate, 
+    InterviewSessionUpdate, 
+    InterviewSessionRead,
+    InterviewStatus
+)
+from .interview_report import (
+    InterviewReport,
+    InterviewReportCreate,
+    InterviewReportUpdate,
+    InterviewReportRead,
+    InterviewReportSummary,
+    RecommendationType
+)
 
 __all__ = [
     "Vacancy",
@@ -14,4 +29,15 @@ __all__ = [
     "Session",
     "SessionCreate",
     "SessionRead",
+    "InterviewSession",
+    "InterviewSessionCreate",
+    "InterviewSessionUpdate", 
+    "InterviewSessionRead",
+    "InterviewStatus",
+    "InterviewReport",
+    "InterviewReportCreate",
+    "InterviewReportUpdate",
+    "InterviewReportRead",
+    "InterviewReportSummary",
+    "RecommendationType",
 ]

app/models/interview.py

@@ -1,7 +1,7 @@
-from sqlmodel import SQLModel, Field, Column
-from sqlalchemy import Enum as SQLEnum, String
+from sqlmodel import SQLModel, Field, Column, Relationship
+from sqlalchemy import Enum as SQLEnum, JSON
 from datetime import datetime
-from typing import Optional
+from typing import Optional, List, Dict, Any
 from enum import Enum
 
 
@@ -18,12 +18,10 @@ class InterviewStatus(str, Enum):
 class InterviewSessionBase(SQLModel):
     resume_id: int = Field(foreign_key="resume.id")
     room_name: str = Field(max_length=255, unique=True)
-    status: str = Field(
-        default="created",
-        sa_column=Column(SQLEnum('created', 'active', 'completed', 'failed', name="interviewstatus"))
-    )
+    status: str = Field(default="created", max_length=50)
     transcript: Optional[str] = None
     ai_feedback: Optional[str] = None
+    dialogue_history: Optional[List[Dict[str, Any]]] = Field(default=None, sa_column=Column(JSON))
     # Добавляем отслеживание AI процесса
     ai_agent_pid: Optional[int] = None
     ai_agent_status: str = Field(default="not_started")  # not_started, running, stopped, failed
@@ -35,6 +33,10 @@ class InterviewSession(InterviewSessionBase, table=True):
     id: Optional[int] = Field(default=None, primary_key=True)
     started_at: datetime = Field(default_factory=datetime.utcnow)
     completed_at: Optional[datetime] = None
+    
+    # Связь с отчетом (один к одному)
+    report: Optional["InterviewReport"] = Relationship(back_populates="interview_session")
+    
 
 
 class InterviewSessionCreate(SQLModel):
@@ -47,6 +49,7 @@ class InterviewSessionUpdate(SQLModel):
     completed_at: Optional[datetime] = None
     transcript: Optional[str] = None
     ai_feedback: Optional[str] = None
+    dialogue_history: Optional[List[Dict[str, Any]]] = None
 
 
 class InterviewSessionRead(InterviewSessionBase):

app/models/interview_report.py

@@ -0,0 +1,207 @@
+# -*- coding: utf-8 -*-
+from sqlmodel import SQLModel, Field, Column, Relationship
+from sqlalchemy import JSON, String, Integer, Float, Text
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+from enum import Enum
+
+
+class RecommendationType(str, Enum):
+    STRONGLY_RECOMMEND = "strongly_recommend"
+    RECOMMEND = "recommend" 
+    CONSIDER = "consider"
+    REJECT = "reject"
+    
+    def __str__(self):
+        return self.value
+
+
+class InterviewReportBase(SQLModel):
+    """Базовая модель отчета по интервью"""
+    interview_session_id: int = Field(foreign_key="interview_sessions.id", unique=True)
+    
+    # Основные критерии оценки (0-100)
+    technical_skills_score: int = Field(ge=0, le=100)
+    technical_skills_justification: Optional[str] = Field(default=None, max_length=1000)
+    technical_skills_concerns: Optional[str] = Field(default=None, max_length=500)
+    
+    experience_relevance_score: int = Field(ge=0, le=100)
+    experience_relevance_justification: Optional[str] = Field(default=None, max_length=1000)
+    experience_relevance_concerns: Optional[str] = Field(default=None, max_length=500)
+    
+    communication_score: int = Field(ge=0, le=100)
+    communication_justification: Optional[str] = Field(default=None, max_length=1000)
+    communication_concerns: Optional[str] = Field(default=None, max_length=500)
+    
+    problem_solving_score: int = Field(ge=0, le=100)
+    problem_solving_justification: Optional[str] = Field(default=None, max_length=1000)
+    problem_solving_concerns: Optional[str] = Field(default=None, max_length=500)
+    
+    cultural_fit_score: int = Field(ge=0, le=100)
+    cultural_fit_justification: Optional[str] = Field(default=None, max_length=1000)
+    cultural_fit_concerns: Optional[str] = Field(default=None, max_length=500)
+    
+    # Агрегированные поля
+    overall_score: int = Field(ge=0, le=100)
+    recommendation: RecommendationType
+    
+    # Дополнительные поля для анализа
+    strengths: Optional[List[str]] = Field(default=None, sa_column=Column(JSON))
+    weaknesses: Optional[List[str]] = Field(default=None, sa_column=Column(JSON))
+    red_flags: Optional[List[str]] = Field(default=None, sa_column=Column(JSON))
+    
+    # Метрики интервью
+    questions_quality_score: Optional[float] = Field(default=None, ge=0, le=10)  # Средняя оценка ответов
+    interview_duration_minutes: Optional[int] = Field(default=None, ge=0)
+    response_count: Optional[int] = Field(default=None, ge=0)
+    dialogue_messages_count: Optional[int] = Field(default=None, ge=0)
+    
+    # Дополнительная информация
+    next_steps: Optional[str] = Field(default=None, max_length=1000)
+    interviewer_notes: Optional[str] = Field(default=None, sa_column=Column(Text))
+    
+    # Детальный анализ вопросов (JSON)
+    questions_analysis: Optional[List[Dict[str, Any]]] = Field(default=None, sa_column=Column(JSON))
+    
+    # Метаданные анализа
+    analysis_method: Optional[str] = Field(default="openai_gpt4", max_length=50)  # openai_gpt4, fallback_heuristic
+    llm_model_used: Optional[str] = Field(default=None, max_length=100)
+    analysis_duration_seconds: Optional[int] = Field(default=None, ge=0)
+
+
+class InterviewReport(InterviewReportBase, table=True):
+    """Полный отчет по интервью с ID и временными метками"""
+    __tablename__ = "interview_reports"
+    
+    id: Optional[int] = Field(default=None, primary_key=True)
+    created_at: datetime = Field(default_factory=datetime.utcnow)
+    updated_at: datetime = Field(default_factory=datetime.utcnow)
+    
+    # Связь с сессией интервью
+    interview_session: Optional["InterviewSession"] = Relationship(back_populates="report")
+
+
+class InterviewReportCreate(SQLModel):
+    """Модель для создания отчета"""
+    interview_session_id: int
+    
+    technical_skills_score: int = Field(ge=0, le=100)
+    technical_skills_justification: Optional[str] = None
+    technical_skills_concerns: Optional[str] = None
+    
+    experience_relevance_score: int = Field(ge=0, le=100)
+    experience_relevance_justification: Optional[str] = None
+    experience_relevance_concerns: Optional[str] = None
+    
+    communication_score: int = Field(ge=0, le=100)
+    communication_justification: Optional[str] = None
+    communication_concerns: Optional[str] = None
+    
+    problem_solving_score: int = Field(ge=0, le=100)
+    problem_solving_justification: Optional[str] = None
+    problem_solving_concerns: Optional[str] = None
+    
+    cultural_fit_score: int = Field(ge=0, le=100)
+    cultural_fit_justification: Optional[str] = None
+    cultural_fit_concerns: Optional[str] = None
+    
+    overall_score: int = Field(ge=0, le=100)
+    recommendation: RecommendationType
+    
+    strengths: Optional[List[str]] = None
+    weaknesses: Optional[List[str]] = None
+    red_flags: Optional[List[str]] = None
+    
+    questions_quality_score: Optional[float] = None
+    interview_duration_minutes: Optional[int] = None
+    response_count: Optional[int] = None
+    dialogue_messages_count: Optional[int] = None
+    
+    next_steps: Optional[str] = None
+    interviewer_notes: Optional[str] = None
+    questions_analysis: Optional[List[Dict[str, Any]]] = None
+    
+    analysis_method: Optional[str] = "openai_gpt4"
+    llm_model_used: Optional[str] = None
+    analysis_duration_seconds: Optional[int] = None
+
+
+class InterviewReportUpdate(SQLModel):
+    """Модель для обновления отчета"""
+    technical_skills_score: Optional[int] = Field(default=None, ge=0, le=100)
+    technical_skills_justification: Optional[str] = None
+    technical_skills_concerns: Optional[str] = None
+    
+    experience_relevance_score: Optional[int] = Field(default=None, ge=0, le=100)
+    experience_relevance_justification: Optional[str] = None
+    experience_relevance_concerns: Optional[str] = None
+    
+    communication_score: Optional[int] = Field(default=None, ge=0, le=100)
+    communication_justification: Optional[str] = None
+    communication_concerns: Optional[str] = None
+    
+    problem_solving_score: Optional[int] = Field(default=None, ge=0, le=100)
+    problem_solving_justification: Optional[str] = None
+    problem_solving_concerns: Optional[str] = None
+    
+    cultural_fit_score: Optional[int] = Field(default=None, ge=0, le=100)
+    cultural_fit_justification: Optional[str] = None
+    cultural_fit_concerns: Optional[str] = None
+    
+    overall_score: Optional[int] = Field(default=None, ge=0, le=100)
+    recommendation: Optional[RecommendationType] = None
+    
+    strengths: Optional[List[str]] = None
+    weaknesses: Optional[List[str]] = None
+    red_flags: Optional[List[str]] = None
+    
+    questions_quality_score: Optional[float] = None
+    interview_duration_minutes: Optional[int] = None
+    response_count: Optional[int] = None
+    dialogue_messages_count: Optional[int] = None
+    
+    next_steps: Optional[str] = None
+    interviewer_notes: Optional[str] = None
+    questions_analysis: Optional[List[Dict[str, Any]]] = None
+    
+    analysis_method: Optional[str] = None
+    llm_model_used: Optional[str] = None
+    analysis_duration_seconds: Optional[int] = None
+
+
+class InterviewReportRead(InterviewReportBase):
+    """Модель для чтения отчета с ID и временными метками"""
+    id: int
+    created_at: datetime
+    updated_at: datetime
+
+
+class InterviewReportSummary(SQLModel):
+    """Краткая сводка отчета для списков"""
+    id: int
+    interview_session_id: int
+    overall_score: int
+    recommendation: RecommendationType
+    created_at: datetime
+    
+    # Основные баллы
+    technical_skills_score: int
+    experience_relevance_score: int
+    communication_score: int
+    problem_solving_score: int
+    cultural_fit_score: int
+    
+    # Краткие выводы
+    strengths: Optional[List[str]] = None
+    red_flags: Optional[List[str]] = None
+
+
+# Индексы для эффективных запросов по скорингу
+"""
+Полезные SQL индексы:
+CREATE INDEX idx_interview_reports_overall_score ON interview_reports (overall_score DESC);
+CREATE INDEX idx_interview_reports_recommendation ON interview_reports (recommendation);
+CREATE INDEX idx_interview_reports_technical_skills ON interview_reports (technical_skills_score DESC);
+CREATE INDEX idx_interview_reports_communication ON interview_reports (communication_score DESC);
+CREATE INDEX idx_interview_reports_session_id ON interview_reports (interview_session_id);
+"""

app/models/resume.py

@@ -56,8 +56,8 @@ class ResumeUpdate(SQLModel):
     status: Optional[ResumeStatus] = None
     interview_report_url: Optional[str] = None
     notes: Optional[str] = None
-    parsed_data: Optional[dict] = Field(default=None, sa_column=Column(JSON))
-    interview_plan: Optional[dict] = Field(default=None, sa_column=Column(JSON))
+    parsed_data: Optional[dict] = None
+    interview_plan: Optional[dict] = None
     parse_error: Optional[str] = None
 
 

app/repositories/__init__.py

@@ -1,4 +1,5 @@
 from .vacancy_repository import VacancyRepository
 from .resume_repository import ResumeRepository
+from .interview_repository import InterviewRepository
 
-__all__ = ["VacancyRepository", "ResumeRepository"]
+__all__ = ["VacancyRepository", "ResumeRepository", "InterviewRepository"]

app/repositories/base_repository.py

@@ -1,49 +1,54 @@
-from typing import TypeVar, Generic, Optional, List, Type
+from typing import TypeVar, Generic, Optional, List, Type, Annotated
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select, update, delete
 from sqlmodel import SQLModel
+from fastapi import Depends
+from app.core.database import get_session
 
 ModelType = TypeVar("ModelType", bound=SQLModel)
 
 
 class BaseRepository(Generic[ModelType]):
-    def __init__(self, model: Type[ModelType], session: AsyncSession):
+    def __init__(self, model: Type[ModelType], session: Annotated[AsyncSession, Depends(get_session)]):
         self.model = model
-        self.session = session
+        self._session = session
 
     async def create(self, obj_in: ModelType) -> ModelType:
         db_obj = self.model.model_validate(obj_in)
-        self.session.add(db_obj)
-        await self.session.commit()
-        await self.session.refresh(db_obj)
+        self._session.add(db_obj)
+        await self._session.commit()
+        await self._session.refresh(db_obj)
         return db_obj
 
     async def get(self, id: int) -> Optional[ModelType]:
         statement = select(self.model).where(self.model.id == id)
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         return result.scalar_one_or_none()
 
     async def get_all(self, skip: int = 0, limit: int = 100) -> List[ModelType]:
         statement = select(self.model).offset(skip).limit(limit)
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         return result.scalars().all()
 
     async def update(self, id: int, obj_in: dict) -> Optional[ModelType]:
-        statement = (
-            update(self.model)
-            .where(self.model.id == id)
-            .values(**obj_in)
-            .returning(self.model)
+        # Получаем объект и обновляем его напрямую
+        result = await self._session.execute(
+            select(self.model).where(self.model.id == id)
         )
-        result = await self.session.execute(statement)
         db_obj = result.scalar_one_or_none()
-        if db_obj:
-            await self.session.commit()
-            await self.session.refresh(db_obj)
+        
+        if not db_obj:
+            return None
+        
+        for key, value in obj_in.items():
+            setattr(db_obj, key, value)
+        
+        await self._session.commit()
+        await self._session.refresh(db_obj)
         return db_obj
 
     async def delete(self, id: int) -> bool:
         statement = delete(self.model).where(self.model.id == id)
-        result = await self.session.execute(statement)
-        await self.session.commit()
+        result = await self._session.execute(statement)
+        await self._session.commit()
         return result.rowcount > 0

app/repositories/interview_repository.py

@@ -0,0 +1,115 @@
+from typing import Optional, List, Annotated
+from datetime import datetime
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, update, desc
+from fastapi import Depends
+from app.core.database import get_session
+from app.models.interview import InterviewSession, InterviewStatus
+from app.repositories.base_repository import BaseRepository
+
+
+class InterviewRepository(BaseRepository[InterviewSession]):
+    def __init__(self, session: Annotated[AsyncSession, Depends(get_session)]):
+        super().__init__(InterviewSession, session)
+    
+    async def get_by_room_name(self, room_name: str) -> Optional[InterviewSession]:
+        """Получить сессию интервью по имени комнаты"""
+        statement = select(InterviewSession).where(InterviewSession.room_name == room_name)
+        result = await self._session.execute(statement)
+        return result.scalar_one_or_none()
+    
+    async def update_status(self, session_id: int, status: str, completed_at: Optional[datetime] = None) -> bool:
+        """Обновить статус сессии"""
+        try:
+            # Получаем объект и обновляем его напрямую
+            result = await self._session.execute(
+                select(InterviewSession).where(InterviewSession.id == session_id)
+            )
+            session_obj = result.scalar_one_or_none()
+            
+            if not session_obj:
+                return False
+            
+            session_obj.status = status
+            if completed_at:
+                session_obj.completed_at = completed_at
+            
+            await self._session.commit()
+            return True
+        except Exception:
+            await self._session.rollback()
+            return False
+    
+    async def update_dialogue_history(self, room_name: str, dialogue_history: list) -> bool:
+        """Обновить историю диалога для сессии"""
+        try:
+            # Получаем объект и обновляем его напрямую
+            result = await self._session.execute(
+                select(InterviewSession).where(InterviewSession.room_name == room_name)
+            )
+            session_obj = result.scalar_one_or_none()
+            
+            if not session_obj:
+                return False
+            
+            session_obj.dialogue_history = dialogue_history
+            await self._session.commit()
+            return True
+        except Exception:
+            await self._session.rollback()
+            return False
+    
+    async def update_ai_agent_status(self, session_id: int, pid: Optional[int] = None, status: str = "not_started") -> bool:
+        """Обновить статус AI агента"""
+        try:
+            # Получаем объект и обновляем его напрямую
+            result = await self._session.execute(
+                select(InterviewSession).where(InterviewSession.id == session_id)
+            )
+            session_obj = result.scalar_one_or_none()
+            
+            if not session_obj:
+                return False
+            
+            session_obj.ai_agent_pid = pid
+            session_obj.ai_agent_status = status
+            await self._session.commit()
+            return True
+        except Exception:
+            await self._session.rollback()
+            return False
+    
+    async def get_sessions_with_running_agents(self) -> List[InterviewSession]:
+        """Получить сессии с запущенными AI агентами"""
+        statement = select(InterviewSession).where(
+            InterviewSession.ai_agent_status == "running"
+        )
+        result = await self._session.execute(statement)
+        return result.scalars().all()
+
+    async def get_active_session_by_resume_id(self, resume_id: int) -> Optional[InterviewSession]:
+        """Получить активную сессию собеседования для резюме"""
+        statement = (
+            select(InterviewSession)
+            .where(InterviewSession.resume_id == resume_id)
+            .where(InterviewSession.status.in_(["created", "active"]))
+            .order_by(InterviewSession.started_at.desc())
+        )
+        result = await self._session.execute(statement)
+        return result.scalar_one_or_none()
+
+    async def create_interview_session(self, resume_id: int, room_name: str) -> InterviewSession:
+        """Создать новую сессию интервью"""
+        from app.models.interview import InterviewSessionCreate
+        session_data = InterviewSessionCreate(
+            resume_id=resume_id,
+            room_name=room_name
+        )
+        return await self.create(session_data.model_dump())
+
+    async def update_session_status(self, session_id: int, status: str) -> bool:
+        """Обновить статус сессии (алиас для update_status)"""
+        completed_at = None
+        if status == "completed":
+            completed_at = datetime.utcnow()
+        return await self.update_status(session_id, status, completed_at)

app/repositories/resume_repository.py

@@ -1,55 +1,55 @@
-from typing import List, Optional
+from typing import List, Optional, Annotated
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
+from fastapi import Depends
+from app.core.database import get_session
 from app.models.resume import Resume, ResumeStatus
 from .base_repository import BaseRepository
 
 
 class ResumeRepository(BaseRepository[Resume]):
-    def __init__(self, session: AsyncSession):
+    def __init__(self, session: Annotated[AsyncSession, Depends(get_session)]):
         super().__init__(Resume, session)
 
     async def get_by_vacancy_id(self, vacancy_id: int) -> List[Resume]:
         statement = select(Resume).where(Resume.vacancy_id == vacancy_id)
-        result = await self.session.execute(statement)
-        return result.scalars().all()
-
-    async def get_by_session_id(self, session_id: int) -> List[Resume]:
-        """Получить все резюме пользователя по session_id"""
-        statement = select(Resume).where(Resume.session_id == session_id)
-        result = await self.session.execute(statement)
-        return result.scalars().all()
-
-    async def get_by_vacancy_and_session(self, vacancy_id: int, session_id: int) -> List[Resume]:
-        """Получить резюме пользователя для конкретной вакансии"""
-        statement = select(Resume).where(
-            Resume.vacancy_id == vacancy_id,
-            Resume.session_id == session_id
-        )
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         return result.scalars().all()
 
     async def get_by_status(self, status: ResumeStatus) -> List[Resume]:
         statement = select(Resume).where(Resume.status == status)
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         return result.scalars().all()
 
-    async def get_by_applicant_email(self, email: str) -> List[Resume]:
-        statement = select(Resume).where(Resume.applicant_email == email)
-        result = await self.session.execute(statement)
-        return result.scalars().all()
+    async def get_by_id(self, resume_id: int) -> Optional[Resume]:
+        """Получить резюме по ID"""
+        return await self.get(resume_id)
 
-    async def create_with_session(self, resume_data: dict, session_id: int) -> Resume:
+    async def create_with_session(self, resume_dict: dict, session_id: int) -> Resume:
         """Создать резюме с привязкой к сессии"""
-        resume_data['session_id'] = session_id
-        new_resume = Resume(**resume_data)
-        return await self.create(new_resume)
+        resume_dict['session_id'] = session_id
+        return await self.create(resume_dict)
+
+    async def get_by_session_id(self, session_id: int) -> List[Resume]:
+        """Получить резюме по session_id"""
+        statement = select(Resume).where(Resume.session_id == session_id)
+        result = await self._session.execute(statement)
+        return result.scalars().all()
+
+    async def get_by_vacancy_and_session(self, vacancy_id: int, session_id: int) -> List[Resume]:
+        """Получить резюме по vacancy_id и session_id"""
+        statement = select(Resume).where(
+            Resume.vacancy_id == vacancy_id,
+            Resume.session_id == session_id
+        )
+        result = await self._session.execute(statement)
+        return result.scalars().all()
 
     async def update_status(self, resume_id: int, status: ResumeStatus) -> Optional[Resume]:
+        """Обновить статус резюме"""
         return await self.update(resume_id, {"status": status})
 
     async def add_interview_report(self, resume_id: int, report_url: str) -> Optional[Resume]:
-        return await self.update(resume_id, {
-            "interview_report_url": report_url,
-            "status": ResumeStatus.INTERVIEWED
-        })
+        """Добавить ссылку на отчет интервью"""
+        return await self.update(resume_id, {"interview_report_url": report_url})
+

app/repositories/session_repository.py

@@ -1,13 +1,15 @@
-from typing import Optional
+from typing import Optional, Annotated
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
+from fastapi import Depends
+from app.core.database import get_session
 from app.models.session import Session
 from app.repositories.base_repository import BaseRepository
 from datetime import datetime
 
 
 class SessionRepository(BaseRepository[Session]):
-    def __init__(self, session: AsyncSession):
+    def __init__(self, session: Annotated[AsyncSession, Depends(get_session)]):
         super().__init__(Session, session)
 
     async def get_by_session_id(self, session_id: str) -> Optional[Session]:
@@ -17,7 +19,7 @@ class SessionRepository(BaseRepository[Session]):
             Session.is_active == True,
             Session.expires_at > datetime.utcnow()
         )
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         return result.scalar_one_or_none()
 
     async def create_session(self, user_agent: Optional[str] = None, ip_address: Optional[str] = None) -> Session:
@@ -31,9 +33,9 @@ class SessionRepository(BaseRepository[Session]):
         if session:
             session.is_active = False
             session.updated_at = datetime.utcnow()
-            self.session.add(session)
-            await self.session.commit()
-            await self.session.refresh(session)
+            self._session.add(session)
+            await self._session.commit()
+            await self._session.refresh(session)
             return True
         return False
 
@@ -43,22 +45,22 @@ class SessionRepository(BaseRepository[Session]):
         if session:
             session.last_activity = datetime.utcnow()
             session.updated_at = datetime.utcnow()
-            self.session.add(session)
-            await self.session.commit()
-            await self.session.refresh(session)
+            self._session.add(session)
+            await self._session.commit()
+            await self._session.refresh(session)
             return True
         return False
 
     async def cleanup_expired_sessions(self) -> int:
         """Remove expired sessions"""
         statement = select(Session).where(Session.expires_at < datetime.utcnow())
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         expired_sessions = result.scalars().all()
         
         count = 0
         for session in expired_sessions:
-            await self.session.delete(session)
+            await self._session.delete(session)
             count += 1
             
-        await self.session.commit()
+        await self._session.commit()
         return count

app/repositories/vacancy_repository.py

@@ -1,34 +1,62 @@
-from typing import List, Optional
+from typing import List, Optional, Annotated
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select, and_
+from fastapi import Depends
+from app.core.database import get_session
 from app.models.vacancy import Vacancy, VacancyCreate, VacancyUpdate
 from .base_repository import BaseRepository
 
 
 class VacancyRepository(BaseRepository[Vacancy]):
-    def __init__(self, session: AsyncSession):
+    def __init__(self, session: Annotated[AsyncSession, Depends(get_session)]):
         super().__init__(Vacancy, session)
 
     async def get_by_company(self, company_name: str) -> List[Vacancy]:
         statement = select(Vacancy).where(Vacancy.company_name == company_name)
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         return result.scalars().all()
 
-    async def get_by_area(self, area_name: str) -> List[Vacancy]:
-        statement = select(Vacancy).where(Vacancy.area_name == area_name)
-        result = await self.session.execute(statement)
-        return result.scalars().all()
-
-    async def get_active_vacancies(self, skip: int = 0, limit: int = 100) -> List[Vacancy]:
+    async def get_active(self, skip: int = 0, limit: int = 100) -> List[Vacancy]:
         statement = (
             select(Vacancy)
             .where(Vacancy.is_archived == False)
             .offset(skip)
             .limit(limit)
         )
-        result = await self.session.execute(statement)
+        result = await self._session.execute(statement)
         return result.scalars().all()
 
+    async def search(
+        self,
+        title: Optional[str] = None,
+        company_name: Optional[str] = None,
+        area_name: Optional[str] = None,
+        skip: int = 0,
+        limit: int = 100
+    ) -> List[Vacancy]:
+        """Поиск вакансий по критериям"""
+        statement = select(Vacancy)
+        conditions = []
+
+        if title:
+            conditions.append(Vacancy.title.ilike(f"%{title}%"))
+        if company_name:
+            conditions.append(Vacancy.company_name.ilike(f"%{company_name}%"))
+        if area_name:
+            conditions.append(Vacancy.area_name.ilike(f"%{area_name}%"))
+
+        if conditions:
+            statement = statement.where(and_(*conditions))
+
+        statement = statement.offset(skip).limit(limit)
+        result = await self._session.execute(statement)
+        return result.scalars().all()
+
+
+    async def get_active_vacancies(self, skip: int = 0, limit: int = 100) -> List[Vacancy]:
+        """Получить активные вакансии (алиас для get_active)"""
+        return await self.get_active(skip=skip, limit=limit)
+
     async def search_vacancies(
         self,
         title: Optional[str] = None,
@@ -37,19 +65,15 @@ class VacancyRepository(BaseRepository[Vacancy]):
         skip: int = 0,
         limit: int = 100
     ) -> List[Vacancy]:
-        conditions = []
-        
-        if title:
-            conditions.append(Vacancy.title.ilike(f"%{title}%"))
-        if company_name:
-            conditions.append(Vacancy.company_name.ilike(f"%{company_name}%"))
-        if area_name:
-            conditions.append(Vacancy.area_name.ilike(f"%{area_name}%"))
-        
-        statement = select(Vacancy)
-        if conditions:
-            statement = statement.where(and_(*conditions))
-        
-        statement = statement.offset(skip).limit(limit)
-        result = await self.session.execute(statement)
-        return result.scalars().all()
+        """Поиск вакансий (алиас для search)"""
+        return await self.search(
+            title=title,
+            company_name=company_name,
+            area_name=area_name,
+            skip=skip,
+            limit=limit
+        )
+
+    async def archive(self, vacancy_id: int) -> Optional[Vacancy]:
+        """Архивировать вакансию"""
+        return await self.update(vacancy_id, {"is_active": False})

app/routers/admin_router.py

@@ -1,117 +1,79 @@
 from fastapi import APIRouter, Depends, HTTPException
-from sqlalchemy.ext.asyncio import AsyncSession
-from app.core.database import get_db
-from app.services.interview_service import InterviewRoomService
-from typing import List, Dict
-import psutil
+from app.services.admin_service import AdminService
+from typing import Dict
 
 router = APIRouter(prefix="/admin", tags=["Admin"])
 
 
 @router.get("/interview-processes")
-async def list_active_interview_processes(db: AsyncSession = Depends(get_db)) -> Dict:
+async def list_active_interview_processes(
+    admin_service: AdminService = Depends(AdminService)
+) -> Dict:
     """Список всех активных AI процессов интервью"""
-    interview_service = InterviewRoomService(db)
-    
-    active_sessions = await interview_service.get_active_agent_processes()
-    
-    processes_info = []
-    for session in active_sessions:
-        process_info = {
-            "session_id": session.id,
-            "resume_id": session.resume_id,
-            "room_name": session.room_name,
-            "pid": session.ai_agent_pid,
-            "status": session.ai_agent_status,
-            "started_at": session.started_at.isoformat() if session.started_at else None,
-            "is_running": False,
-            "memory_mb": 0,
-            "cpu_percent": 0
-        }
-        
-        # Проверяем реальное состояние процесса
-        if session.ai_agent_pid:
-            try:
-                process = psutil.Process(session.ai_agent_pid)
-                if process.is_running():
-                    process_info["is_running"] = True
-                    process_info["memory_mb"] = round(process.memory_info().rss / 1024 / 1024, 1)
-                    process_info["cpu_percent"] = round(process.cpu_percent(), 1)
-            except (psutil.NoSuchProcess, psutil.AccessDenied):
-                pass
-        
-        processes_info.append(process_info)
-    
-    return {
-        "total_active_sessions": len(active_sessions),
-        "processes": processes_info
-    }
+    return await admin_service.get_active_interview_processes()
 
 
 @router.post("/interview-processes/{session_id}/stop")
-async def stop_interview_process(session_id: int, db: AsyncSession = Depends(get_db)) -> Dict:
+async def stop_interview_process(
+    session_id: int, 
+    admin_service: AdminService = Depends(AdminService)
+) -> Dict:
     """Остановить AI процесс для конкретного интервью"""
-    interview_service = InterviewRoomService(db)
+    result = await admin_service.stop_interview_process(session_id)
     
-    success = await interview_service.stop_agent_process(session_id)
+    if not result["success"]:
+        raise HTTPException(status_code=404, detail=result["message"])
     
-    if success:
-        return {"message": f"AI process for session {session_id} stopped successfully"}
-    else:
-        raise HTTPException(status_code=404, detail=f"Session {session_id} not found or process not running")
+    return result
 
 
 @router.post("/interview-processes/cleanup")
-async def cleanup_dead_processes(db: AsyncSession = Depends(get_db)) -> Dict:
+async def cleanup_dead_processes(
+    admin_service: AdminService = Depends(AdminService)
+) -> Dict:
     """Очистка мертвых процессов"""
-    interview_service = InterviewRoomService(db)
-    
-    cleaned_count = await interview_service.cleanup_dead_processes()
-    
-    return {
-        "message": f"Cleaned up {cleaned_count} dead processes"
-    }
+    return await admin_service.cleanup_dead_processes()
 
 
 @router.get("/system-stats")
-async def get_system_stats() -> Dict:
+async def get_system_stats(
+    admin_service: AdminService = Depends(AdminService)
+) -> Dict:
     """Общая статистика системы"""
-    try:
-        # Общая информация о системе
-        cpu_percent = psutil.cpu_percent(interval=1)
-        memory = psutil.virtual_memory()
-        disk = psutil.disk_usage('/')
-        
-        # Поиск всех Python процессов (потенциальные AI агенты)
-        python_processes = []
-        for proc in psutil.process_iter(['pid', 'name', 'memory_info', 'cpu_percent', 'cmdline']):
-            try:
-                if proc.info['name'] and 'python' in proc.info['name'].lower():
-                    cmdline = ' '.join(proc.info['cmdline']) if proc.info['cmdline'] else ''
-                    if 'ai_interviewer_agent' in cmdline:
-                        python_processes.append({
-                            'pid': proc.info['pid'],
-                            'memory_mb': round(proc.info['memory_info'].rss / 1024 / 1024, 1),
-                            'cpu_percent': proc.info['cpu_percent'] or 0,
-                            'cmdline': cmdline
-                        })
-            except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
-                pass
-        
-        return {
-            "system": {
-                "cpu_percent": cpu_percent,
-                "memory_percent": memory.percent,
-                "memory_available_gb": round(memory.available / 1024 / 1024 / 1024, 1),
-                "disk_percent": disk.percent,
-                "disk_free_gb": round(disk.free / 1024 / 1024 / 1024, 1)
-            },
-            "ai_agents": {
-                "count": len(python_processes),
-                "total_memory_mb": sum(p['memory_mb'] for p in python_processes),
-                "processes": python_processes
-            }
-        }
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error getting system stats: {str(e)}")
+    result = await admin_service.get_system_stats()
+    
+    if "error" in result:
+        raise HTTPException(status_code=500, detail=result["error"])
+    
+    return result
+
+
+@router.get("/analytics/dashboard")
+async def get_analytics_dashboard(
+    admin_service: AdminService = Depends(AdminService)
+) -> Dict:
+    """Основная аналитическая панель"""
+    return await admin_service.get_analytics_dashboard()
+
+
+@router.get("/analytics/candidates/{vacancy_id}")
+async def get_vacancy_analytics(
+    vacancy_id: int, 
+    admin_service: AdminService = Depends(AdminService)
+) -> Dict:
+    """Аналитика кандидатов по конкретной вакансии"""
+    return await admin_service.get_vacancy_analytics(vacancy_id)
+
+
+@router.post("/analytics/generate-reports/{vacancy_id}")
+async def generate_reports_for_vacancy(
+    vacancy_id: int, 
+    admin_service: AdminService = Depends(AdminService)
+) -> Dict:
+    """Запустить генерацию отчетов для всех кандидатов вакансии"""
+    result = await admin_service.generate_reports_for_vacancy(vacancy_id)
+    
+    if "error" in result:
+        raise HTTPException(status_code=404, detail=result["error"])
+    
+    return result

app/routers/analysis_router.py

@@ -0,0 +1,225 @@
+# -*- coding: utf-8 -*-
+from typing import List
+from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
+from pydantic import BaseModel
+
+from app.repositories.resume_repository import ResumeRepository
+from celery_worker.interview_analysis_task import generate_interview_report, analyze_multiple_candidates
+
+
+router = APIRouter(
+    prefix="/analysis",
+    tags=["analysis"]
+)
+
+
+class AnalysisResponse(BaseModel):
+    """Ответ запуска задачи анализа"""
+    message: str
+    resume_id: int
+    task_id: str
+
+
+class BulkAnalysisRequest(BaseModel):
+    """Запрос массового анализа"""
+    resume_ids: List[int]
+
+
+class BulkAnalysisResponse(BaseModel):
+    """Ответ массового анализа"""
+    message: str
+    resume_count: int
+    task_id: str
+
+
+class CandidateRanking(BaseModel):
+    """Рейтинг кандидата"""
+    resume_id: int
+    candidate_name: str
+    overall_score: int
+    recommendation: str
+    position: str
+
+
+@router.post("/interview-report/{resume_id}", response_model=AnalysisResponse)
+async def start_interview_analysis(
+    resume_id: int,
+    background_tasks: BackgroundTasks,
+    resume_repo: ResumeRepository = Depends(ResumeRepository)
+):
+    """
+    Запускает анализ интервью для конкретного кандидата
+    
+    Анализирует:
+    - Соответствие резюме вакансии
+    - Качество ответов в диалоге интервью  
+    - Технические навыки и опыт
+    - Коммуникативные способности
+    - Общую рекомендацию и рейтинг
+    """
+    
+    # Проверяем, существует ли резюме
+    resume = await resume_repo.get_by_id(resume_id)
+    
+    if not resume:
+        raise HTTPException(status_code=404, detail="Resume not found")
+    
+    # Запускаем задачу анализа
+    task = generate_interview_report.delay(resume_id)
+    
+    return AnalysisResponse(
+        message="Interview analysis started",
+        resume_id=resume_id,
+        task_id=task.id
+    )
+
+
+@router.post("/bulk-analysis", response_model=BulkAnalysisResponse)
+async def start_bulk_analysis(
+    request: BulkAnalysisRequest,
+    background_tasks: BackgroundTasks,
+    resume_repo: ResumeRepository = Depends(ResumeRepository)
+):
+    """
+    Запускает массовый анализ нескольких кандидатов
+    
+    Возвращает ранжированный список кандидатов по общему баллу
+    Полезно для сравнения кандидатов на одну позицию
+    """
+    
+    # Проверяем, что все резюме существуют
+    existing_resumes = []
+    
+    for resume_id in request.resume_ids:
+        resume = await resume_repo.get_by_id(resume_id)
+        if resume:
+            existing_resumes.append(resume_id)
+    
+    if not existing_resumes:
+        raise HTTPException(status_code=404, detail="No valid resumes found")
+    
+    # Запускаем задачу массового анализа
+    task = analyze_multiple_candidates.delay(existing_resumes)
+    
+    return BulkAnalysisResponse(
+        message="Bulk analysis started",
+        resume_count=len(existing_resumes),
+        task_id=task.id
+    )
+
+
+@router.get("/ranking/{vacancy_id}")
+async def get_candidates_ranking(
+    vacancy_id: int,
+    resume_repo: ResumeRepository = Depends(ResumeRepository)
+):
+    """
+    Получить ранжированный список кандидатов для вакансии
+    
+    Сортирует кандидатов по результатам анализа интервью
+    Показывает только тех, кто прошел интервью
+    """
+    
+    # Получаем все резюме для вакансии со статусом "interviewed"
+    resumes = await resume_repo.get_by_vacancy_id(vacancy_id)
+    interviewed_resumes = [r for r in resumes if r.status in ["interviewed"]]
+    
+    if not interviewed_resumes:
+        return {
+            "vacancy_id": vacancy_id,
+            "candidates": [],
+            "message": "No interviewed candidates found"
+        }
+    
+    # Запускаем массовый анализ если еще не было
+    resume_ids = [r.id for r in interviewed_resumes]
+    task = analyze_multiple_candidates.delay(resume_ids)
+    
+    # В реальности здесь нужно дождаться выполнения или получить из кэша
+    # Пока возвращаем информацию о запущенной задаче
+    return {
+        "vacancy_id": vacancy_id,
+        "task_id": task.id,
+        "message": f"Analysis started for {len(resume_ids)} candidates",
+        "resume_ids": resume_ids
+    }
+
+
+@router.get("/report/{resume_id}")
+async def get_interview_report(
+    resume_id: int,
+    resume_repo: ResumeRepository = Depends(ResumeRepository)
+):
+    """
+    Получить готовый отчет анализа интервью
+    
+    Если отчет еще не готов - запускает анализ
+    """
+    
+    resume = await resume_repo.get_by_id(resume_id)
+    
+    if not resume:
+        raise HTTPException(status_code=404, detail="Resume not found")
+    
+    # Проверяем, есть ли уже готовый отчет в notes
+    if resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes:
+        return {
+            "resume_id": resume_id,
+            "candidate_name": resume.applicant_name,
+            "status": "completed",
+            "report_summary": resume.notes,
+            "message": "Report available"
+        }
+    
+    # Если отчета нет - запускаем анализ
+    task = generate_interview_report.delay(resume_id)
+    
+    return {
+        "resume_id": resume_id,
+        "candidate_name": resume.applicant_name,
+        "status": "in_progress",
+        "task_id": task.id,
+        "message": "Analysis started, check back later"
+    }
+
+
+@router.get("/statistics/{vacancy_id}")
+async def get_analysis_statistics(
+    vacancy_id: int,
+    resume_repo: ResumeRepository = Depends(ResumeRepository)
+):
+    """
+    Получить статистику анализа кандидатов по вакансии
+    """
+    
+    resumes = await resume_repo.get_by_vacancy_id(vacancy_id)
+    
+    total_candidates = len(resumes)
+    interviewed = len([r for r in resumes if r.status == "interviewed"])
+    with_reports = len([r for r in resumes if r.notes and "ОЦЕНКА КАНДИДАТА" in r.notes])
+    
+    # Подсчитываем рекомендации из notes (упрощенно)
+    recommendations = {"strongly_recommend": 0, "recommend": 0, "consider": 0, "reject": 0}
+    
+    for resume in resumes:
+        if resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes:
+            notes = resume.notes.lower()
+            if "strongly_recommend" in notes:
+                recommendations["strongly_recommend"] += 1
+            elif "recommend" in notes and "strongly_recommend" not in notes:
+                recommendations["recommend"] += 1
+            elif "consider" in notes:
+                recommendations["consider"] += 1
+            elif "reject" in notes:
+                recommendations["reject"] += 1
+    
+    return {
+        "vacancy_id": vacancy_id,
+        "statistics": {
+            "total_candidates": total_candidates,
+            "interviewed_candidates": interviewed,
+            "analyzed_candidates": with_reports,
+            "recommendations": recommendations,
+            "analysis_completion": round((with_reports / max(interviewed, 1)) * 100, 1) if interviewed > 0 else 0
+        }
+    }

app/routers/interview_router.py

@@ -1,6 +1,5 @@
 from fastapi import APIRouter, Depends, HTTPException, Request
-from sqlalchemy.ext.asyncio import AsyncSession
-from app.core.session_middleware import get_current_session, get_db_session
+from app.core.session_middleware import get_current_session
 from app.models.session import Session
 from app.models.interview import InterviewValidationResponse, LiveKitTokenResponse, InterviewStatus
 from app.services.interview_service import InterviewRoomService
@@ -13,14 +12,12 @@ async def validate_interview(
     request: Request,
     resume_id: int,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    interview_service: InterviewRoomService = Depends(InterviewRoomService)
 ):
     """Валидация резюме для проведения собеседования"""
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
     
-    interview_service = InterviewRoomService(db_session)
-    
     # Проверяем валидность резюме для собеседования
     validation_result = await interview_service.validate_resume_for_interview(resume_id)
     
@@ -40,14 +37,12 @@ async def get_interview_token(
     request: Request,
     resume_id: int,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    interview_service: InterviewRoomService = Depends(InterviewRoomService)
 ):
     """Получение токена для LiveKit собеседования"""
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
     
-    interview_service = InterviewRoomService(db_session)
-    
     # Получаем токен для LiveKit
     token_response = await interview_service.get_livekit_token(resume_id)
     
@@ -65,14 +60,12 @@ async def end_interview(
     request: Request,
     resume_id: int,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    interview_service: InterviewRoomService = Depends(InterviewRoomService)
 ):
     """Завершение собеседования"""
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
     
-    interview_service = InterviewRoomService(db_session)
-    
     # Получаем активную сессию собеседования
     interview_session = await interview_service.get_interview_session(resume_id)
     

app/routers/resume_router.py

@@ -1,7 +1,6 @@
 from fastapi import APIRouter, Depends, HTTPException, Query, UploadFile, File, Form, Request
-from sqlalchemy.ext.asyncio import AsyncSession
 from typing import List, Optional
-from app.core.session_middleware import get_current_session, get_db_session
+from app.core.session_middleware import get_current_session
 from app.models.resume import ResumeCreate, ResumeUpdate, ResumeRead, ResumeStatus
 from app.models.session import Session
 from app.services.resume_service import ResumeService
@@ -22,13 +21,12 @@ async def create_resume(
     cover_letter: Optional[str] = Form(None),
     resume_file: UploadFile = File(...),
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    resume_service: ResumeService = Depends(ResumeService)
 ):
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
     
     file_service = FileService()
-    resume_service = ResumeService(db_session)
     
     upload_result = await file_service.upload_resume_file(resume_file)
     if not upload_result:
@@ -74,13 +72,11 @@ async def get_resumes(
     vacancy_id: Optional[int] = Query(None),
     status: Optional[ResumeStatus] = Query(None),
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    service: ResumeService = Depends(ResumeService)
 ):
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
     
-    service = ResumeService(db_session)
-    
     # Получаем только резюме текущего пользователя
     if vacancy_id:
         return await service.get_resumes_by_vacancy_and_session(vacancy_id, current_session.id)
@@ -93,12 +89,10 @@ async def get_resume(
     request: Request,
     resume_id: int,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    service: ResumeService = Depends(ResumeService)
 ):
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
-    
-    service = ResumeService(db_session)
     resume = await service.get_resume(resume_id)
     
     if not resume:
@@ -117,12 +111,10 @@ async def update_resume(
     resume_id: int,
     resume: ResumeUpdate,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    service: ResumeService = Depends(ResumeService)
 ):
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
-    
-    service = ResumeService(db_session)
     existing_resume = await service.get_resume(resume_id)
     
     if not existing_resume:
@@ -142,12 +134,10 @@ async def update_resume_status(
     resume_id: int,
     status: ResumeStatus,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    service: ResumeService = Depends(ResumeService)
 ):
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
-    
-    service = ResumeService(db_session)
     existing_resume = await service.get_resume(resume_id)
     
     if not existing_resume:
@@ -167,13 +157,12 @@ async def upload_interview_report(
     resume_id: int,
     report_file: UploadFile = File(...),
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    resume_service: ResumeService = Depends(ResumeService)
 ):
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
     
     file_service = FileService()
-    resume_service = ResumeService(db_session)
     
     existing_resume = await resume_service.get_resume(resume_id)
     if not existing_resume:
@@ -198,14 +187,13 @@ async def get_parsing_status(
     resume_id: int,
     task_id: str = Query(..., description="Task ID from resume upload response"),
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    service: ResumeService = Depends(ResumeService)
 ):
     """Получить статус парсинга резюме по task_id"""
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
     
     # Проверяем доступ к резюме
-    service = ResumeService(db_session)
     resume = await service.get_resume(resume_id)
     
     if not resume:
@@ -263,12 +251,10 @@ async def delete_resume(
     request: Request,
     resume_id: int,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    service: ResumeService = Depends(ResumeService)
 ):
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
-    
-    service = ResumeService(db_session)
     existing_resume = await service.get_resume(resume_id)
     
     if not existing_resume:

app/routers/session_router.py

@@ -1,7 +1,6 @@
 from fastapi import APIRouter, Depends, Request, HTTPException
 from fastapi.responses import JSONResponse
-from sqlalchemy.ext.asyncio import AsyncSession
-from app.core.session_middleware import get_current_session, get_db_session
+from app.core.session_middleware import get_current_session
 from app.repositories.session_repository import SessionRepository
 from app.models.session import Session, SessionRead
 from typing import Optional
@@ -38,18 +37,15 @@ async def get_current_session_info(
 async def refresh_session(
     request: Request,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    session_repo: SessionRepository = Depends(SessionRepository)
 ):
     """Продлить сессию на 30 дней"""
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
-    
-    session_repo = SessionRepository(db_session)
     current_session.extend_session(days=30)
     
-    db_session.add(current_session)
-    db_session.commit()
-    db_session.refresh(current_session)
+    # Обновляем через репозиторий
+    await session_repo.update_last_activity(current_session.session_id)
     
     logger.info(f"Extended session {current_session.session_id}")
     
@@ -64,13 +60,11 @@ async def refresh_session(
 async def logout(
     request: Request,
     current_session: Session = Depends(get_current_session),
-    db_session: AsyncSession = Depends(get_db_session)
+    session_repo: SessionRepository = Depends(SessionRepository)
 ):
     """Завершить текущую сессию"""
     if not current_session:
         raise HTTPException(status_code=401, detail="No active session")
-    
-    session_repo = SessionRepository(db_session)
     deactivated = await session_repo.deactivate_session(current_session.session_id)
     
     if deactivated:

app/routers/vacancy_router.py

@@ -1,9 +1,8 @@
 from fastapi import APIRouter, Depends, HTTPException, Query
-from sqlalchemy.ext.asyncio import AsyncSession
 from typing import List, Optional
-from app.core.database import get_session
-from app.models.vacancy import VacancyCreate, VacancyUpdate, VacancyRead
+
 from app.services.vacancy_service import VacancyService
+from app.models.vacancy import VacancyCreate, VacancyUpdate, VacancyRead
 
 router = APIRouter(prefix="/vacancies", tags=["vacancies"])
 
@@ -11,10 +10,9 @@ router = APIRouter(prefix="/vacancies", tags=["vacancies"])
 @router.post("/", response_model=VacancyRead)
 async def create_vacancy(
     vacancy: VacancyCreate,
-    session: AsyncSession = Depends(get_session)
+    vacancy_service: VacancyService = Depends(VacancyService)
 ):
-    service = VacancyService(session)
-    return await service.create_vacancy(vacancy)
+    return await vacancy_service.create_vacancy(vacancy)
 
 
 @router.get("/", response_model=List[VacancyRead])
@@ -25,12 +23,10 @@ async def get_vacancies(
     title: Optional[str] = Query(None),
     company_name: Optional[str] = Query(None),
     area_name: Optional[str] = Query(None),
-    session: AsyncSession = Depends(get_session)
+    vacancy_service: VacancyService = Depends(VacancyService)
 ):
-    service = VacancyService(session)
-    
     if any([title, company_name, area_name]):
-        return await service.search_vacancies(
+        return await vacancy_service.search_vacancies(
             title=title,
             company_name=company_name,
             area_name=area_name,
@@ -39,18 +35,17 @@ async def get_vacancies(
         )
     
     if active_only:
-        return await service.get_active_vacancies(skip=skip, limit=limit)
+        return await vacancy_service.get_active_vacancies(skip=skip, limit=limit)
     
-    return await service.get_all_vacancies(skip=skip, limit=limit)
+    return await vacancy_service.get_all_vacancies(skip=skip, limit=limit)
 
 
 @router.get("/{vacancy_id}", response_model=VacancyRead)
 async def get_vacancy(
     vacancy_id: int,
-    session: AsyncSession = Depends(get_session)
+    vacancy_service: VacancyService = Depends(VacancyService)
 ):
-    service = VacancyService(session)
-    vacancy = await service.get_vacancy(vacancy_id)
+    vacancy = await vacancy_service.get_vacancy(vacancy_id)
     if not vacancy:
         raise HTTPException(status_code=404, detail="Vacancy not found")
     return vacancy
@@ -60,10 +55,9 @@ async def get_vacancy(
 async def update_vacancy(
     vacancy_id: int,
     vacancy: VacancyUpdate,
-    session: AsyncSession = Depends(get_session)
+    vacancy_service: VacancyService = Depends(VacancyService)
 ):
-    service = VacancyService(session)
-    updated_vacancy = await service.update_vacancy(vacancy_id, vacancy)
+    updated_vacancy = await vacancy_service.update_vacancy(vacancy_id, vacancy)
     if not updated_vacancy:
         raise HTTPException(status_code=404, detail="Vacancy not found")
     return updated_vacancy
@@ -72,10 +66,9 @@ async def update_vacancy(
 @router.delete("/{vacancy_id}")
 async def delete_vacancy(
     vacancy_id: int,
-    session: AsyncSession = Depends(get_session)
+    vacancy_service: VacancyService = Depends(VacancyService)
 ):
-    service = VacancyService(session)
-    success = await service.delete_vacancy(vacancy_id)
+    success = await vacancy_service.delete_vacancy(vacancy_id)
     if not success:
         raise HTTPException(status_code=404, detail="Vacancy not found")
     return {"message": "Vacancy deleted successfully"}
@@ -84,10 +77,9 @@ async def delete_vacancy(
 @router.patch("/{vacancy_id}/archive", response_model=VacancyRead)
 async def archive_vacancy(
     vacancy_id: int,
-    session: AsyncSession = Depends(get_session)
+    vacancy_service: VacancyService = Depends(VacancyService)
 ):
-    service = VacancyService(session)
-    archived_vacancy = await service.archive_vacancy(vacancy_id)
+    archived_vacancy = await vacancy_service.archive_vacancy(vacancy_id)
     if not archived_vacancy:
         raise HTTPException(status_code=404, detail="Vacancy not found")
     return archived_vacancy

app/services/admin_service.py

@@ -0,0 +1,257 @@
+from typing import Dict, List, Annotated
+from fastapi import Depends
+
+from app.repositories.interview_repository import InterviewRepository
+from app.repositories.resume_repository import ResumeRepository
+from app.services.interview_service import InterviewRoomService
+from app.services.interview_finalization_service import InterviewFinalizationService
+
+
+class AdminService:
+    def __init__(
+        self,
+        interview_repo: Annotated[InterviewRepository, Depends(InterviewRepository)],
+        resume_repo: Annotated[ResumeRepository, Depends(ResumeRepository)],
+        interview_service: Annotated[InterviewRoomService, Depends(InterviewRoomService)],
+        finalization_service: Annotated[InterviewFinalizationService, Depends(InterviewFinalizationService)]
+    ):
+        self.interview_repo = interview_repo
+        self.resume_repo = resume_repo
+        self.interview_service = interview_service
+        self.finalization_service = finalization_service
+
+    async def get_active_interview_processes(self):
+        """Получить список активных AI процессов"""
+        active_sessions = await self.interview_service.get_active_agent_processes()
+        
+        import psutil
+        processes_info = []
+        
+        for session in active_sessions:
+            process_info = {
+                "session_id": session.id,
+                "resume_id": session.resume_id,
+                "room_name": session.room_name,
+                "pid": session.ai_agent_pid,
+                "status": session.ai_agent_status,
+                "started_at": session.started_at.isoformat() if session.started_at else None,
+                "is_running": False,
+                "memory_mb": 0,
+                "cpu_percent": 0
+            }
+            
+            if session.ai_agent_pid:
+                try:
+                    process = psutil.Process(session.ai_agent_pid)
+                    if process.is_running():
+                        process_info["is_running"] = True
+                        process_info["memory_mb"] = round(process.memory_info().rss / 1024 / 1024, 1)
+                        process_info["cpu_percent"] = round(process.cpu_percent(interval=0.1), 1)
+                except (psutil.NoSuchProcess, psutil.AccessDenied):
+                    pass
+                    
+            processes_info.append(process_info)
+        
+        return {
+            "active_processes": len([p for p in processes_info if p["is_running"]]),
+            "total_sessions": len(processes_info),
+            "processes": processes_info
+        }
+
+    async def stop_interview_process(self, session_id: int):
+        """Остановить AI процесс интервью"""
+        success = await self.interview_service.stop_agent_process(session_id)
+        
+        return {
+            "success": success,
+            "message": f"Process for session {session_id} {'stopped' if success else 'failed to stop'}"
+        }
+
+    async def cleanup_dead_processes(self):
+        """Очистить информацию о мертвых процессах"""
+        cleaned_count = await self.finalization_service.cleanup_dead_processes()
+        
+        return {
+            "cleaned_processes": cleaned_count,
+            "message": f"Cleaned up {cleaned_count} dead processes"
+        }
+
+    async def get_analytics_dashboard(self) -> Dict:
+        """Основная аналитическая панель"""
+        
+        all_resumes = await self.resume_repo.get_all()
+        
+        status_stats = {}
+        for resume in all_resumes:
+            status = resume.status.value if hasattr(resume.status, 'value') else str(resume.status)
+            status_stats[status] = status_stats.get(status, 0) + 1
+        
+        analyzed_count = 0
+        recommendation_stats = {"strongly_recommend": 0, "recommend": 0, "consider": 0, "reject": 0}
+        
+        for resume in all_resumes:
+            if resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes:
+                analyzed_count += 1
+                notes = resume.notes.lower()
+                
+                if "strongly_recommend" in notes:
+                    recommendation_stats["strongly_recommend"] += 1
+                elif "recommend" in notes and "strongly_recommend" not in notes:
+                    recommendation_stats["recommend"] += 1
+                elif "consider" in notes:
+                    recommendation_stats["consider"] += 1
+                elif "reject" in notes:
+                    recommendation_stats["reject"] += 1
+        
+        recent_resumes = sorted(all_resumes, key=lambda x: x.updated_at, reverse=True)[:10]
+        recent_activity = []
+        
+        for resume in recent_resumes:
+            activity_item = {
+                "resume_id": resume.id,
+                "candidate_name": resume.applicant_name,
+                "status": resume.status.value if hasattr(resume.status, 'value') else str(resume.status),
+                "updated_at": resume.updated_at.isoformat() if resume.updated_at else None,
+                "has_analysis": resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes
+            }
+            recent_activity.append(activity_item)
+        
+        return {
+            "summary": {
+                "total_candidates": len(all_resumes),
+                "interviewed_candidates": status_stats.get("interviewed", 0),
+                "analyzed_candidates": analyzed_count,
+                "analysis_completion_rate": round((analyzed_count / max(len(all_resumes), 1)) * 100, 1)
+            },
+            "status_distribution": status_stats,
+            "recommendation_distribution": recommendation_stats,
+            "recent_activity": recent_activity
+        }
+
+    async def get_vacancy_analytics(self, vacancy_id: int) -> Dict:
+        """Аналитика кандидатов по конкретной вакансии"""
+        
+        vacancy_resumes = await self.resume_repo.get_by_vacancy_id(vacancy_id)
+        
+        if not vacancy_resumes:
+            return {
+                "vacancy_id": vacancy_id,
+                "message": "No candidates found for this vacancy",
+                "candidates": []
+            }
+        
+        candidates_info = []
+        
+        for resume in vacancy_resumes:
+            overall_score = None
+            recommendation = None
+            
+            if resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes:
+                notes = resume.notes
+                if "Общий балл:" in notes:
+                    try:
+                        score_line = [line for line in notes.split('\n') if "Общий балл:" in line][0]
+                        overall_score = int(score_line.split("Общий балл:")[1].split("/")[0].strip())
+                    except:
+                        pass
+                
+                if "Рекомендация:" in notes:
+                    try:
+                        rec_line = [line for line in notes.split('\n') if "Рекомендация:" in line][0]
+                        recommendation = rec_line.split("Рекомендация:")[1].strip()
+                    except:
+                        pass
+            
+            candidate_info = {
+                "resume_id": resume.id,
+                "candidate_name": resume.applicant_name,
+                "email": resume.applicant_email,
+                "status": resume.status.value if hasattr(resume.status, 'value') else str(resume.status),
+                "created_at": resume.created_at.isoformat() if resume.created_at else None,
+                "updated_at": resume.updated_at.isoformat() if resume.updated_at else None,
+                "has_analysis": resume.notes and "ОЦЕНКА КАНДИДАТА" in resume.notes,
+                "overall_score": overall_score,
+                "recommendation": recommendation,
+                "has_parsed_data": bool(resume.parsed_data),
+                "has_interview_plan": bool(resume.interview_plan)
+            }
+            
+            candidates_info.append(candidate_info)
+        
+        candidates_info.sort(key=lambda x: (x['overall_score'] or 0, x['updated_at'] or ''), reverse=True)
+        
+        return {
+            "vacancy_id": vacancy_id,
+            "total_candidates": len(candidates_info),
+            "candidates": candidates_info
+        }
+
+    async def generate_reports_for_vacancy(self, vacancy_id: int) -> Dict:
+        """Запустить генерацию отчетов для всех кандидатов вакансии"""
+        
+        from celery_worker.interview_analysis_task import analyze_multiple_candidates
+        
+        vacancy_resumes = await self.resume_repo.get_by_vacancy_id(vacancy_id)
+        
+        interviewed_resumes = [r for r in vacancy_resumes if r.status in ["interviewed"]]
+        
+        if not interviewed_resumes:
+            return {
+                "error": "No interviewed candidates found for this vacancy",
+                "vacancy_id": vacancy_id
+            }
+        
+        resume_ids = [r.id for r in interviewed_resumes]
+        
+        task = analyze_multiple_candidates.delay(resume_ids)
+        
+        return {
+            "vacancy_id": vacancy_id,
+            "task_id": task.id,
+            "message": f"Analysis started for {len(resume_ids)} candidates",
+            "resume_ids": resume_ids
+        }
+
+    async def get_system_stats(self) -> Dict:
+        """Общая статистика системы"""
+        import psutil
+        
+        try:
+            cpu_percent = psutil.cpu_percent(interval=1)
+            memory = psutil.virtual_memory()
+            disk = psutil.disk_usage('/')
+            
+            python_processes = []
+            for proc in psutil.process_iter(['pid', 'name', 'memory_info', 'cpu_percent', 'cmdline']):
+                try:
+                    if proc.info['name'] and 'python' in proc.info['name'].lower():
+                        cmdline = ' '.join(proc.info['cmdline']) if proc.info['cmdline'] else ''
+                        if 'ai_interviewer_agent' in cmdline:
+                            python_processes.append({
+                                'pid': proc.info['pid'],
+                                'memory_mb': round(proc.info['memory_info'].rss / 1024 / 1024, 1),
+                                'cpu_percent': proc.info['cpu_percent'] or 0,
+                                'cmdline': cmdline
+                            })
+                except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
+                    pass
+            
+            return {
+                "system": {
+                    "cpu_percent": cpu_percent,
+                    "memory_percent": memory.percent,
+                    "memory_available_gb": round(memory.available / 1024 / 1024 / 1024, 1),
+                    "disk_percent": disk.percent,
+                    "disk_free_gb": round(disk.free / 1024 / 1024 / 1024, 1)
+                },
+                "ai_agents": {
+                    "count": len(python_processes),
+                    "total_memory_mb": sum(p['memory_mb'] for p in python_processes),
+                    "processes": python_processes
+                }
+            }
+            
+        except Exception as e:
+            return {
+                "error": f"Error getting system stats: {str(e)}"
+            }

app/services/ai_interviewer_service.py

@@ -104,16 +104,7 @@ class AIInterviewerService:
                 pass
         except Exception as e:
             logger.error(f"Error processing user audio: {str(e)}")
-    
-    async def speech_to_text(self, audio_data: bytes) -> Optional[str]:
-        """Конвертация речи в текст через Whisper API"""
-        # TODO: Интеграция с OpenAI Whisper или другим STT сервисом
-        pass
-    
-    async def text_to_speech(self, text: str) -> bytes:
-        """Конвертация текста в речь через TTS сервис"""
-        # TODO: Интеграция с ElevenLabs, OpenAI TTS или другим сервисом
-        pass
+
     
     async def generate_interview_questions(self):
         """Генерация вопросов для интервью на основе резюме"""

app/services/interview_finalization_service.py

@@ -0,0 +1,197 @@
+from typing import Optional, Annotated
+from datetime import datetime
+import logging
+from fastapi import Depends
+
+from app.repositories.interview_repository import InterviewRepository
+from app.repositories.resume_repository import ResumeRepository
+from app.models.resume import ResumeStatus
+
+logger = logging.getLogger("interview-finalization")
+
+
+class InterviewFinalizationService:
+    """Сервис для завершения интервью и запуска анализа"""
+    
+    def __init__(
+        self,
+        interview_repo: Annotated[InterviewRepository, Depends(InterviewRepository)],
+        resume_repo: Annotated[ResumeRepository, Depends(ResumeRepository)]
+    ):
+        self.interview_repo = interview_repo
+        self.resume_repo = resume_repo
+    
+    async def finalize_interview(
+        self, 
+        room_name: str, 
+        dialogue_history: list,
+        interview_metrics: dict = None
+    ) -> Optional[dict]:
+        """
+        Завершает интервью и запускает анализ
+        
+        Args:
+            room_name: Имя комнаты LiveKit
+            dialogue_history: История диалога
+            interview_metrics: Метрики интервью (количество вопросов, время и т.д.)
+        
+        Returns:
+            dict с информацией о завершенном интервью или None если ошибка
+        """
+        try:
+            logger.info(f"[FINALIZE] Starting finalization for room: {room_name}")
+            
+            # 1. Находим сессию интервью
+            interview_session = await self.interview_repo.get_by_room_name(room_name)
+            if not interview_session:
+                logger.error(f"[FINALIZE] Interview session not found for room: {room_name}")
+                return None
+            
+            # 2. Обновляем статус сессии интервью на "completed"
+            success = await self.interview_repo.update_status(
+                interview_session.id, 
+                "completed", 
+                datetime.utcnow()
+            )
+            
+            if not success:
+                logger.error(f"[FINALIZE] Failed to update session status for {interview_session.id}")
+                return None
+            
+            resume_id = interview_session.resume_id
+            logger.info(f"[FINALIZE] Interview session {interview_session.id} marked as completed")
+            
+            # 3. Обновляем статус резюме на "INTERVIEWED"
+            resume = await self.resume_repo.get(resume_id)
+            if resume:
+                await self.resume_repo.update(resume_id, {
+                    "status": ResumeStatus.INTERVIEWED,
+                    "updated_at": datetime.utcnow()
+                })
+                logger.info(f"[FINALIZE] Resume {resume_id} status updated to INTERVIEWED")
+            else:
+                logger.warning(f"[FINALIZE] Resume {resume_id} not found")
+            
+            # 4. Сохраняем финальную историю диалога
+            await self.interview_repo.update_dialogue_history(room_name, dialogue_history)
+            logger.info(f"[FINALIZE] Saved final dialogue ({len(dialogue_history)} messages)")
+            
+            # 5. Обновляем статус AI агента
+            await self.interview_repo.update_ai_agent_status(interview_session.id, None, "stopped")
+            
+            # 6. Запускаем анализ интервью через Celery
+            analysis_task = await self._start_interview_analysis(resume_id)
+            
+            # 7. Собираем итоговые метрики
+            finalization_result = {
+                "session_id": interview_session.id,
+                "resume_id": resume_id,
+                "room_name": room_name,
+                "total_messages": len(dialogue_history),
+                "analysis_task_id": analysis_task.get('task_id') if analysis_task else None,
+                "completed_at": datetime.utcnow().isoformat(),
+                "metrics": interview_metrics or {}
+            }
+            
+            logger.info(f"[FINALIZE] Interview successfully finalized: {finalization_result}")
+            return finalization_result
+            
+        except Exception as e:
+            logger.error(f"[FINALIZE] Error finalizing interview for room {room_name}: {str(e)}")
+            return None
+    
+    async def _start_interview_analysis(self, resume_id: int):
+        """Запускает анализ интервью через Celery"""
+        # try:
+        logger.info(f"[FINALIZE] Attempting to start analysis task for resume_id: {resume_id}")
+            
+            # Импортируем задачу
+        #     from celery_worker.interview_analysis_task import generate_interview_report
+        #     logger.debug(f"[FINALIZE] Successfully imported generate_interview_report task")
+        #
+        #     # Запускаем задачу
+        #     task = generate_interview_report.delay(resume_id)
+        #     logger.info(f"[FINALIZE] Analysis task started: {task.id} for resume_id: {resume_id}")
+        #     return task
+        #
+        # except ImportError as e:
+        #     logger.error(f"[FINALIZE] Import error for analysis task: {str(e)}")
+        #     return None
+        # except ConnectionError as e:
+        #     logger.error(f"[FINALIZE] Connection error starting analysis task for resume {resume_id}: {str(e)}")
+        #     logger.warning(f"[FINALIZE] This may indicate Redis/Celery broker is not accessible from AI agent process")
+        #
+        #     # Fallback: попытка запуска анализа через HTTP API
+        #     return await self._start_analysis_via_http(resume_id)
+        # except Exception as e:
+        #     logger.error(f"[FINALIZE] Failed to start analysis task for resume {resume_id}: {str(e)}")
+        #     logger.debug(f"[FINALIZE] Exception type: {type(e).__name__}")
+            
+            # Fallback: попытка запуска анализа через HTTP API для любых других ошибок
+        return await self._start_analysis_via_http(resume_id)
+    
+    async def _start_analysis_via_http(self, resume_id: int):
+        """Fallback: запуск анализа через HTTP API (когда Celery недоступен из AI агента)"""
+        try:
+            import httpx
+            
+            url = f"http://localhost:8000/api/v1/analysis/interview-report/{resume_id}"
+            logger.info(f"[FINALIZE] Attempting HTTP fallback to URL: {url}")
+            
+            # Попробуем отправить HTTP запрос на локальный API для запуска анализа
+            async with httpx.AsyncClient() as client:
+                response = await client.post(url, timeout=5.0)
+                
+                if response.status_code == 200:
+                    result = response.json()
+                    logger.info(f"[FINALIZE] Analysis started via HTTP API for resume_id: {resume_id}, task_id: {result.get('task_id', 'unknown')}")
+                    return result
+                else:
+                    logger.error(f"[FINALIZE] HTTP API returned {response.status_code} for resume_id: {resume_id}")
+                    logger.debug(f"[FINALIZE] Response body: {response.text[:200]}")
+                    return None
+                    
+        except Exception as e:
+            logger.error(f"[FINALIZE] HTTP fallback failed for resume {resume_id}: {str(e)}")
+            return None
+    
+    async def save_dialogue_to_session(self, room_name: str, dialogue_history: list) -> bool:
+        """Сохраняет диалог в сессию (для промежуточных сохранений)"""
+        try:
+            success = await self.interview_repo.update_dialogue_history(room_name, dialogue_history)
+            if success:
+                logger.info(f"[DIALOGUE] Saved {len(dialogue_history)} messages for room: {room_name}")
+            return success
+        except Exception as e:
+            logger.error(f"[DIALOGUE] Error saving dialogue for room {room_name}: {str(e)}")
+            return False
+    
+    async def cleanup_dead_processes(self) -> int:
+        """Очищает информацию о мертвых AI процессах"""
+        try:
+            import psutil
+            
+            active_sessions = await self.interview_repo.get_sessions_with_running_agents()
+            cleaned_count = 0
+            
+            for session in active_sessions:
+                if session.ai_agent_pid:
+                    try:
+                        process = psutil.Process(session.ai_agent_pid)
+                        if not process.is_running():
+                            await self.interview_repo.update_ai_agent_status(
+                                session.id, None, "stopped"
+                            )
+                            cleaned_count += 1
+                    except psutil.NoSuchProcess:
+                        await self.interview_repo.update_ai_agent_status(
+                            session.id, None, "stopped"
+                        )
+                        cleaned_count += 1
+            
+            logger.info(f"[CLEANUP] Cleaned up {cleaned_count} dead processes")
+            return cleaned_count
+            
+        except Exception as e:
+            logger.error(f"[CLEANUP] Error cleaning up processes: {str(e)}")
+            return 0

app/services/interview_service.py

@@ -3,27 +3,30 @@ import time
 import uuid
 import json
 import subprocess
-from typing import Optional
-from datetime import datetime, timedelta
+from typing import Optional, Annotated
+from datetime import datetime
 from livekit.api import AccessToken, VideoGrants
-from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy import select
+from fastapi import Depends
+from app.repositories.interview_repository import InterviewRepository
+from app.repositories.resume_repository import ResumeRepository
 from app.models.interview import (
     InterviewSession, 
-    InterviewSessionCreate, 
-    InterviewSessionUpdate,
-    InterviewStatus,
+    InterviewSessionCreate,
     InterviewValidationResponse,
     LiveKitTokenResponse
 )
 from app.models.resume import Resume, ResumeStatus
-from app.models.vacancy import Vacancy
 from rag.settings import settings
 
 
 class InterviewRoomService:
-    def __init__(self, db_session: AsyncSession):
-        self.db = db_session
+    def __init__(
+        self,
+        interview_repo: Annotated[InterviewRepository, Depends(InterviewRepository)],
+        resume_repo: Annotated[ResumeRepository, Depends(ResumeRepository)]
+    ):
+        self.interview_repo = interview_repo
+        self.resume_repo = resume_repo
         self.livekit_url = settings.livekit_url or "ws://localhost:7880"
         self.api_key = settings.livekit_api_key or "devkey"
         self.api_secret = settings.livekit_api_secret or "secret"
@@ -32,8 +35,7 @@ class InterviewRoomService:
         """Проверяет, можно ли проводить собеседование для данного резюме"""
         try:
             # Получаем резюме
-            result = await self.db.execute(select(Resume).where(Resume.id == resume_id))
-            resume = result.scalar_one_or_none()
+            resume = await self.resume_repo.get(resume_id)
             
             if not resume:
                 return InterviewValidationResponse(
@@ -49,12 +51,7 @@ class InterviewRoomService:
                 )
             
             # Проверяем активную сессию только для информации (не блокируем)
-            result = await self.db.execute(
-                select(InterviewSession)
-                .where(InterviewSession.resume_id == resume_id)
-                .where(InterviewSession.status == "active")
-            )
-            active_session = result.scalar_one_or_none()
+            active_session = await self.interview_repo.get_active_session_by_resume_id(resume_id)
             
             message = "Resume is ready for interview"
             if active_session:
@@ -79,21 +76,12 @@ class InterviewRoomService:
             timestamp = int(time.time())
             room_name = f"interview_{resume_id}_{timestamp}_{unique_id}"
             
-            # Создаем сессию в БД
-            session_data = InterviewSessionCreate(
-                resume_id=resume_id,
-                room_name=room_name
-            )
-            
-            interview_session = InterviewSession(**session_data.model_dump())
-            self.db.add(interview_session)
-            await self.db.commit()
-            await self.db.refresh(interview_session)
+            # Создаем сессию в БД через репозиторий
+            interview_session = await self.interview_repo.create_interview_session(resume_id, room_name)
             
             return interview_session
             
         except Exception as e:
-            await self.db.rollback()
             print(f"Error creating interview session: {str(e)}")
             return None
     
@@ -125,7 +113,7 @@ class InterviewRoomService:
                 return None
             
             # Проверяем, есть ли уже созданная сессия для этого резюме
-            existing_session = await self.get_interview_session(resume_id)
+            existing_session = await self.interview_repo.get_active_session_by_resume_id(resume_id)
             if existing_session:
                 # Используем существующую сессию
                 interview_session = existing_session
@@ -148,7 +136,7 @@ class InterviewRoomService:
             interview_plan = await self.get_resume_data_for_interview(resume_id)
             
             # Обновляем статус сессии на ACTIVE
-            await self.update_session_status(interview_session.id, "active")
+            await self.interview_repo.update_session_status(interview_session.id, "active")
             
             # Запускаем AI агента для этой сессии
             await self.start_ai_interviewer(interview_session, interview_plan)
@@ -165,41 +153,11 @@ class InterviewRoomService:
     
     async def update_session_status(self, session_id: int, status: str) -> bool:
         """Обновляет статус сессии собеседования"""
-        try:
-            result = await self.db.execute(
-                select(InterviewSession).where(InterviewSession.id == session_id)
-            )
-            session = result.scalar_one_or_none()
-            
-            if not session:
-                return False
-            
-            session.status = status
-            if status == "completed":
-                session.completed_at = datetime.utcnow()
-            
-            await self.db.commit()
-            return True
-            
-        except Exception as e:
-            await self.db.rollback()
-            print(f"Error updating session status: {str(e)}")
-            return False
+        return await self.interview_repo.update_session_status(session_id, status)
     
     async def get_interview_session(self, resume_id: int) -> Optional[InterviewSession]:
         """Получает активную сессию собеседования для резюме"""
-        try:
-            result = await self.db.execute(
-                select(InterviewSession)
-                .where(InterviewSession.resume_id == resume_id)
-                .where(InterviewSession.status.in_(["created", "active"]))
-                .order_by(InterviewSession.started_at.desc())
-            )
-            return result.scalar_one_or_none()
-            
-        except Exception as e:
-            print(f"Error getting interview session: {str(e)}")
-            return None
+        return await self.interview_repo.get_active_session_by_resume_id(resume_id)
     
     async def start_ai_interviewer(self, interview_session: InterviewSession, interview_plan: dict):
         """Запускает AI интервьюера для сессии"""
@@ -210,11 +168,14 @@ class InterviewRoomService:
                 f"ai_interviewer_{interview_session.id}"
             )
             
-            # Подготавливаем метаданные с планом интервью
-            room_metadata = json.dumps({
-                "interview_plan": interview_plan,
-                "session_id": interview_session.id
-            })
+            # Сохраняем метаданные во временный файл для избежания проблем с кодировкой
+            import tempfile
+            metadata_file = f"interview_metadata_{interview_session.id}.json"
+            with open(metadata_file, 'w', encoding='utf-8') as f:
+                json.dump({
+                    "interview_plan": interview_plan,
+                    "session_id": interview_session.id
+                }, f, ensure_ascii=False, indent=2)
             
             # Запускаем AI агента в отдельном процессе
             agent_cmd = [
@@ -231,10 +192,11 @@ class InterviewRoomService:
             # Устанавливаем переменные окружения
             env = os.environ.copy()
             env.update({
-                "LIVEKIT_ROOM_METADATA": room_metadata,
+                "INTERVIEW_METADATA_FILE": metadata_file,
                 "OPENAI_API_KEY": settings.openai_api_key or "",
                 "DEEPGRAM_API_KEY": settings.deepgram_api_key or "",
                 "CARTESIA_API_KEY": settings.cartesia_api_key or "",
+                "PYTHONIOENCODING": "utf-8",
             })
             
             # Запускаем процесс в фоне
@@ -251,7 +213,7 @@ class InterviewRoomService:
             print(f"[DEBUG] Started AI interviewer process {process.pid} for session {interview_session.id}")
             
             # Сохраняем PID процесса в БД для управления
-            await self.update_agent_process_info(
+            await self.interview_repo.update_ai_agent_status(
                 interview_session.id, 
                 process.pid, 
                 "running"
@@ -260,7 +222,7 @@ class InterviewRoomService:
         except Exception as e:
             print(f"Error starting AI interviewer: {str(e)}")
             # Обновляем статус на failed
-            await self.update_agent_process_info(
+            await self.interview_repo.update_ai_agent_status(
                 interview_session.id, 
                 None, 
                 "failed"
@@ -270,10 +232,7 @@ class InterviewRoomService:
         """Получает готовый план интервью из базы данных"""
         try:
             # Получаем резюме с готовым планом интервью
-            result = await self.db.execute(
-                select(Resume).where(Resume.id == resume_id)
-            )
-            resume = result.scalar_one_or_none()
+            resume = await self.resume_repo.get(resume_id)
             
             if not resume:
                 return self._get_fallback_interview_plan()
@@ -361,46 +320,16 @@ class InterviewRoomService:
     
     async def update_agent_process_info(self, session_id: int, pid: int = None, status: str = "not_started") -> bool:
         """Обновляет информацию о процессе AI агента"""
-        try:
-            result = await self.db.execute(
-                select(InterviewSession).where(InterviewSession.id == session_id)
-            )
-            session = result.scalar_one_or_none()
-            
-            if not session:
-                return False
-            
-            session.ai_agent_pid = pid
-            session.ai_agent_status = status
-            
-            await self.db.commit()
-            return True
-            
-        except Exception as e:
-            await self.db.rollback()
-            print(f"Error updating agent process info: {str(e)}")
-            return False
+        return await self.interview_repo.update_ai_agent_status(session_id, pid, status)
     
     async def get_active_agent_processes(self) -> list:
         """Получает список активных AI процессов"""
-        try:
-            result = await self.db.execute(
-                select(InterviewSession)
-                .where(InterviewSession.ai_agent_status == "running")
-            )
-            return result.scalars().all()
-            
-        except Exception as e:
-            print(f"Error getting active processes: {str(e)}")
-            return []
+        return await self.interview_repo.get_sessions_with_running_agents()
     
     async def stop_agent_process(self, session_id: int) -> bool:
         """Останавливает AI процесс для сессии"""
         try:
-            result = await self.db.execute(
-                select(InterviewSession).where(InterviewSession.id == session_id)
-            )
-            session = result.scalar_one_or_none()
+            session = await self.interview_repo.get(session_id)
             
             if not session or not session.ai_agent_pid:
                 return False
@@ -423,14 +352,14 @@ class InterviewRoomService:
                     process.kill()
                 
                 # Обновляем статус в БД
-                await self.update_agent_process_info(session_id, None, "stopped")
+                await self.interview_repo.update_ai_agent_status(session_id, None, "stopped")
                 
                 print(f"Stopped AI agent process {session.ai_agent_pid} for session {session_id}")
                 return True
                 
             except (psutil.NoSuchProcess, psutil.AccessDenied):
                 # Процесс уже не существует
-                await self.update_agent_process_info(session_id, None, "stopped")
+                await self.interview_repo.update_ai_agent_status(session_id, None, "stopped")
                 return True
                 
         except Exception as e:
@@ -450,10 +379,10 @@ class InterviewRoomService:
                     try:
                         process = psutil.Process(session.ai_agent_pid)
                         if not process.is_running():
-                            await self.update_agent_process_info(session.id, None, "stopped")
+                            await self.interview_repo.update_ai_agent_status(session.id, None, "stopped")
                             cleaned_count += 1
                     except psutil.NoSuchProcess:
-                        await self.update_agent_process_info(session.id, None, "stopped")
+                        await self.interview_repo.update_ai_agent_status(session.id, None, "stopped")
                         cleaned_count += 1
             
             print(f"Cleaned up {cleaned_count} dead processes")

app/services/resume_service.py

@@ -1,12 +1,12 @@
-from typing import List, Optional
-from sqlalchemy.ext.asyncio import AsyncSession
+from typing import List, Optional, Annotated
+from fastapi import Depends
 from app.models.resume import Resume, ResumeCreate, ResumeUpdate, ResumeStatus
 from app.repositories.resume_repository import ResumeRepository
 
 
 class ResumeService:
-    def __init__(self, session: AsyncSession):
-        self.repository = ResumeRepository(session)
+    def __init__(self, repository: Annotated[ResumeRepository, Depends(ResumeRepository)]):
+        self.repository = repository
 
     async def create_resume(self, resume_data: ResumeCreate) -> Resume:
         resume = Resume.model_validate(resume_data)

app/services/vacancy_service.py

@@ -1,12 +1,12 @@
-from typing import List, Optional
-from sqlalchemy.ext.asyncio import AsyncSession
+from typing import List, Optional, Annotated
+from fastapi import Depends
 from app.models.vacancy import Vacancy, VacancyCreate, VacancyUpdate
 from app.repositories.vacancy_repository import VacancyRepository
 
 
 class VacancyService:
-    def __init__(self, session: AsyncSession):
-        self.repository = VacancyRepository(session)
+    def __init__(self, repository: Annotated[VacancyRepository, Depends(VacancyRepository)]):
+        self.repository = repository
 
     async def create_vacancy(self, vacancy_data: VacancyCreate) -> Vacancy:
         vacancy = Vacancy.model_validate(vacancy_data)

celery_worker/database.py

@@ -8,7 +8,8 @@ from rag.settings import settings
 sync_engine = create_engine(
     settings.database_url.replace("asyncpg", "psycopg2"),  # Убираем asyncpg для синхронного подключения
     echo=False,
-    future=True
+    future=True,
+    connect_args={"client_encoding": "utf8"}  # Принудительно UTF-8
 )
 
 # Создаем синхронный session maker
@@ -59,7 +60,6 @@ class SyncResumeRepository:
                 resume.status = ResumeStatus.PARSED
                 if parsed_data:
                     resume.parsed_data = parsed_data
-                    # НЕ перезаписываем контактные данные из формы - они уже правильные
             elif status == 'failed':
                 resume.status = ResumeStatus.PARSE_FAILED
                 if error_message:
@@ -84,4 +84,30 @@ class SyncResumeRepository:
             self.session.add(resume)
             return resume
         
-        return None
+        return None
+    
+    def _normalize_utf8_dict(self, data):
+        """Нормализует UTF-8 в словаре рекурсивно"""
+        import json
+        
+        # Сериализуем в JSON с ensure_ascii=False, потом парсим обратно
+        # Это принудительно конвертирует все unicode escape sequences в нормальные символы
+        try:
+            json_str = json.dumps(data, ensure_ascii=False, separators=(',', ':'))
+            return json.loads(json_str)
+        except (TypeError, ValueError):
+            # Fallback - рекурсивная обработка
+            if isinstance(data, dict):
+                return {key: self._normalize_utf8_dict(value) for key, value in data.items()}
+            elif isinstance(data, list):
+                return [self._normalize_utf8_dict(item) for item in data]
+            elif isinstance(data, str):
+                try:
+                    # Пытаемся декодировать unicode escape sequences
+                    if '\\u' in data:
+                        return data.encode().decode('unicode_escape')
+                    return data
+                except (UnicodeDecodeError, UnicodeEncodeError):
+                    return data
+            else:
+                return data

celery_worker/interview_analysis_task.py

@@ -0,0 +1,633 @@
+# -*- coding: utf-8 -*-
+import json
+import logging
+from datetime import datetime
+from typing import Dict, Any, List, Optional
+
+from celery import shared_task
+from rag.settings import settings
+from celery_worker.database import get_sync_session, SyncResumeRepository
+
+logger = logging.getLogger(__name__)
+
+
+@shared_task
+def generate_interview_report(resume_id: int):
+    """
+    Комплексная оценка кандидата на основе резюме, вакансии и диалога интервью
+    
+    Args:
+        resume_id: ID резюме для анализа
+        
+    Returns:
+        dict: Полный отчет с оценками и рекомендациями
+    """
+    logger.info(f"[INTERVIEW_ANALYSIS] Starting analysis for resume_id: {resume_id}")
+    
+    try:
+        with get_sync_session() as db:
+            repo = SyncResumeRepository(db)
+            
+            # Получаем данные резюме
+            resume = repo.get_by_id(resume_id)
+            if not resume:
+                logger.error(f"[INTERVIEW_ANALYSIS] Resume {resume_id} not found")
+                return {"error": "Resume not found"}
+            
+            # Получаем данные вакансии (если нет - используем пустые данные)
+            vacancy = _get_vacancy_data(db, resume.vacancy_id)
+            if not vacancy:
+                logger.warning(f"[INTERVIEW_ANALYSIS] Vacancy {resume.vacancy_id} not found, using empty vacancy data")
+                vacancy = {
+                    'id': resume.vacancy_id,
+                    'title': 'Неизвестная позиция',
+                    'description': 'Описание недоступно',
+                    'requirements': [],
+                    'skills_required': [],
+                    'experience_level': 'middle'
+                }
+            
+            # Получаем историю интервью
+            interview_session = _get_interview_session(db, resume_id)
+            
+            # Парсим JSON данные
+            parsed_resume = _parse_json_field(resume.parsed_data)
+            interview_plan = _parse_json_field(resume.interview_plan)
+            dialogue_history = _parse_json_field(interview_session.dialogue_history) if interview_session else []
+            
+            # Генерируем отчет
+            report = _generate_comprehensive_report(
+                resume_id=resume_id,
+                candidate_name=resume.applicant_name,
+                vacancy=vacancy,
+                parsed_resume=parsed_resume,
+                interview_plan=interview_plan,
+                dialogue_history=dialogue_history
+            )
+            
+            # Сохраняем отчет в БД
+            _save_report_to_db(db, resume_id, report)
+            
+            logger.info(f"[INTERVIEW_ANALYSIS] Analysis completed for resume_id: {resume_id}, score: {report['overall_score']}")
+            return report
+            
+    except Exception as e:
+        logger.error(f"[INTERVIEW_ANALYSIS] Error analyzing resume {resume_id}: {str(e)}")
+        return {"error": str(e)}
+
+
+def _get_vacancy_data(db, vacancy_id: int) -> Optional[Dict]:
+    """Получить данные вакансии"""
+    try:
+        from app.models.vacancy import Vacancy
+        vacancy = db.query(Vacancy).filter(Vacancy.id == vacancy_id).first()
+        if vacancy:
+            # Парсим key_skills в список, если это строка
+            key_skills = []
+            if vacancy.key_skills:
+                if isinstance(vacancy.key_skills, str):
+                    # Разделяем по запятым и очищаем от пробелов
+                    key_skills = [skill.strip() for skill in vacancy.key_skills.split(',') if skill.strip()]
+                elif isinstance(vacancy.key_skills, list):
+                    key_skills = vacancy.key_skills
+            
+            # Маппинг Experience enum в строку уровня опыта
+            experience_mapping = {
+                'noExperience': 'junior',
+                'between1And3': 'junior', 
+                'between3And6': 'middle',
+                'moreThan6': 'senior'
+            }
+            experience_level = experience_mapping.get(vacancy.experience, 'middle')
+            
+            return {
+                'id': vacancy.id,
+                'title': vacancy.title,
+                'description': vacancy.description,
+                'requirements': [vacancy.description] if vacancy.description else [],  # Используем описание как требования
+                'skills_required': key_skills,
+                'experience_level': experience_level,
+                'employment_type': vacancy.employment_type,
+                'salary_range': f"{vacancy.salary_from or 0}-{vacancy.salary_to or 0}" if vacancy.salary_from or vacancy.salary_to else None
+            }
+        return None
+    except Exception as e:
+        logger.error(f"Error getting vacancy data: {e}")
+        return None
+
+
+def _get_interview_session(db, resume_id: int):
+    """Получить сессию интервью"""
+    try:
+        from app.models.interview import InterviewSession
+        return db.query(InterviewSession).filter(InterviewSession.resume_id == resume_id).first()
+    except Exception as e:
+        logger.error(f"Error getting interview session: {e}")
+        return None
+
+
+def _parse_json_field(field_data) -> Dict:
+    """Безопасный парсинг JSON поля"""
+    if field_data is None:
+        return {}
+    if isinstance(field_data, dict):
+        return field_data
+    if isinstance(field_data, str):
+        try:
+            return json.loads(field_data)
+        except (json.JSONDecodeError, TypeError):
+            return {}
+    return {}
+
+
+def _generate_comprehensive_report(
+    resume_id: int,
+    candidate_name: str,
+    vacancy: Dict,
+    parsed_resume: Dict,
+    interview_plan: Dict,
+    dialogue_history: List[Dict]
+) -> Dict[str, Any]:
+    """
+    Генерирует комплексный отчет о кандидате с использованием LLM
+    """
+    
+    # Подготавливаем контекст для анализа
+    context = _prepare_analysis_context(
+        vacancy=vacancy,
+        parsed_resume=parsed_resume,
+        interview_plan=interview_plan,
+        dialogue_history=dialogue_history
+    )
+    
+    # Генерируем оценку через OpenAI
+    evaluation = _call_openai_for_evaluation(context)
+    
+    # Формируем финальный отчет
+    report = {
+        "resume_id": resume_id,
+        "candidate_name": candidate_name,
+        "position": vacancy.get('title', 'Unknown Position'),
+        "interview_date": datetime.utcnow().isoformat(),
+        "analysis_context": {
+            "has_parsed_resume": bool(parsed_resume),
+            "has_interview_plan": bool(interview_plan),
+            "dialogue_messages_count": len(dialogue_history),
+            "vacancy_requirements_count": len(vacancy.get('requirements', []))
+        }
+    }
+    
+    # Добавляем результаты оценки
+    if evaluation:
+        # Убеждаемся, что есть overall_score
+        if 'overall_score' not in evaluation:
+            evaluation['overall_score'] = _calculate_overall_score(evaluation)
+        
+        report.update(evaluation)
+    else:
+        # Fallback оценка, если LLM не сработал
+        report.update(_generate_fallback_evaluation(
+            parsed_resume, vacancy, dialogue_history
+        ))
+    
+    return report
+
+
+def _calculate_overall_score(evaluation: Dict) -> int:
+    """Вычисляет общий балл как среднее арифметическое всех критериев"""
+    try:
+        scores = evaluation.get('scores', {})
+        if not scores:
+            return 50  # Default score
+        
+        total_score = 0
+        count = 0
+        
+        for criterion_name, criterion_data in scores.items():
+            if isinstance(criterion_data, dict) and 'score' in criterion_data:
+                total_score += criterion_data['score']
+                count += 1
+        
+        if count == 0:
+            return 50  # Default if no valid scores
+        
+        overall = int(total_score / count)
+        return max(0, min(100, overall))  # Ensure 0-100 range
+        
+    except Exception:
+        return 50  # Safe fallback
+
+
+def _prepare_analysis_context(
+    vacancy: Dict,
+    parsed_resume: Dict,
+    interview_plan: Dict,
+    dialogue_history: List[Dict]
+) -> str:
+    """Подготавливает контекст для анализа LLM"""
+    
+    # Собираем диалог интервью
+    dialogue_text = ""
+    if dialogue_history:
+        dialogue_messages = []
+        for msg in dialogue_history[-20:]:  # Последние 20 сообщений
+            role = msg.get('role', 'unknown')
+            content = msg.get('content', '')
+            dialogue_messages.append(f"{role.upper()}: {content}")
+        dialogue_text = "\n".join(dialogue_messages)
+    
+    # Формируем контекст
+    context = f"""
+АНАЛИЗ КАНДИДАТА НА СОБЕСЕДОВАНИЕ
+
+ВАКАНСИЯ:
+- Позиция: {vacancy.get('title', 'Не указана')}
+- Описание: {vacancy.get('description', 'Не указано')[:500]}
+- Требования: {', '.join(vacancy.get('requirements', []))}
+- Требуемые навыки: {', '.join(vacancy.get('skills_required', []))}
+- Уровень опыта: {vacancy.get('experience_level', 'middle')}
+
+РЕЗЮМЕ КАНДИДАТА:
+- Имя: {parsed_resume.get('name', 'Не указано')}
+- Опыт работы: {parsed_resume.get('total_years', 'Не указано')} лет
+- Навыки: {', '.join(parsed_resume.get('skills', []))}
+- Образование: {parsed_resume.get('education', 'Не указано')}
+- Предыдущие позиции: {'; '.join([pos.get('title', '') + ' в ' + pos.get('company', '') for pos in parsed_resume.get('work_experience', [])])}
+
+ПЛАН ИНТЕРВЬЮ:
+{json.dumps(interview_plan, ensure_ascii=False, indent=2) if interview_plan else 'План интервью не найден'}
+
+ДИАЛОГ ИНТЕРВЬЮ:
+{dialogue_text if dialogue_text else 'Диалог интервью не найден или пуст'}
+"""
+    
+    return context
+
+
+def _call_openai_for_evaluation(context: str) -> Optional[Dict]:
+    """Вызывает OpenAI для генерации оценки"""
+    
+    if not settings.openai_api_key:
+        logger.warning("OpenAI API key not configured, skipping LLM evaluation")
+        return None
+    
+    try:
+        import openai
+        openai.api_key = settings.openai_api_key
+        
+        evaluation_prompt = f"""
+{context}
+
+ЗАДАЧА:
+Проанализируй кандидата и дай оценку по критериям (0-100):
+1. technical_skills: Соответствие техническим требованиям
+2. experience_relevance: Релевантность опыта
+3. communication: Коммуникативные навыки (на основе диалога)
+4. problem_solving: Навыки решения задач
+5. cultural_fit: Соответствие корпоративной культуре
+
+Для каждого критерия:
+- score: оценка 0-100
+- justification: обоснование с примерами из резюме/интервью
+- concerns: возможные риски
+
+Дай итоговую рекомендацию:
+- strongly_recommend (90-100)
+- recommend (70-89) 
+- consider (50-69)
+- reject (0-49)
+
+Вычисли ОБЩИЙ БАЛЛ (overall_score) от 0 до 100 как среднее арифметическое всех 5 критериев.
+
+И топ 3 сильные/слабые стороны.
+
+ОТВЕТЬ СТРОГО В JSON ФОРМАТЕ с обязательными полями:
+- scores: объект с 5 критериями, каждый содержит score, justification, concerns
+- overall_score: число от 0 до 100 (среднее арифметическое всех scores)
+- recommendation: одно из 4 значений выше
+- strengths: массив из 3 сильных сторон
+- weaknesses: массив из 3 слабых сторон
+"""
+
+        response = openai.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": evaluation_prompt}],
+            response_format={"type": "json_object"},
+            temperature=0.3
+        )
+        
+        evaluation = json.loads(response.choices[0].message.content)
+        logger.info(f"[INTERVIEW_ANALYSIS] OpenAI evaluation completed")
+        return evaluation
+        
+    except Exception as e:
+        logger.error(f"[INTERVIEW_ANALYSIS] Error calling OpenAI: {str(e)}")
+        return None
+
+
+def _generate_fallback_evaluation(
+    parsed_resume: Dict, 
+    vacancy: Dict, 
+    dialogue_history: List[Dict]
+) -> Dict[str, Any]:
+    """Генерирует базовую оценку без LLM"""
+    
+    # Простая эвристическая оценка
+    technical_score = _calculate_technical_match(parsed_resume, vacancy)
+    experience_score = _calculate_experience_score(parsed_resume, vacancy)
+    communication_score = 70  # Средняя оценка, если нет диалога
+    
+    if dialogue_history:
+        communication_score = min(90, 50 + len(dialogue_history) * 2)  # Больше диалога = лучше коммуникация
+    
+    overall_score = (technical_score + experience_score + communication_score) // 3
+    
+    # Определяем рекомендацию
+    if overall_score >= 90:
+        recommendation = "strongly_recommend"
+    elif overall_score >= 70:
+        recommendation = "recommend"
+    elif overall_score >= 50:
+        recommendation = "consider"
+    else:
+        recommendation = "reject"
+    
+    return {
+        "scores": {
+            "technical_skills": {
+                "score": technical_score,
+                "justification": f"Соответствие по навыкам: {technical_score}%",
+                "concerns": "Автоматическая оценка без анализа LLM"
+            },
+            "experience_relevance": {
+                "score": experience_score,
+                "justification": f"Опыт работы: {parsed_resume.get('total_years', 0)} лет",
+                "concerns": "Требуется ручная проверка релевантности опыта"
+            },
+            "communication": {
+                "score": communication_score,
+                "justification": f"Активность в диалоге: {len(dialogue_history)} сообщений",
+                "concerns": "Оценка основана на количестве сообщений"
+            },
+            "problem_solving": {
+                "score": 60,
+                "justification": "Средняя оценка (нет данных для анализа)",
+                "concerns": "Требуется техническое интервью"
+            },
+            "cultural_fit": {
+                "score": 65,
+                "justification": "Средняя оценка (нет данных для анализа)",
+                "concerns": "Требуется личная встреча с командой"
+            }
+        },
+        "overall_score": overall_score,
+        "recommendation": recommendation,
+        "strengths": [
+            f"Опыт работы: {parsed_resume.get('total_years', 0)} лет",
+            f"Технические навыки: {len(parsed_resume.get('skills', []))} навыков",
+            f"Участие в интервью: {len(dialogue_history)} сообщений"
+        ],
+        "weaknesses": [
+            "Автоматическая оценка без LLM анализа",
+            "Требуется дополнительное техническое интервью",
+            "Нет глубокого анализа ответов на вопросы"
+        ],
+        "red_flags": [],
+        "next_steps": "Рекомендуется провести техническое интервью с тимлидом для более точной оценки.",
+        "analysis_method": "fallback_heuristic"
+    }
+
+
+def _calculate_technical_match(parsed_resume: Dict, vacancy: Dict) -> int:
+    """Вычисляет соответствие технических навыков"""
+    
+    resume_skills = set([skill.lower() for skill in parsed_resume.get('skills', [])])
+    required_skills = set([skill.lower() for skill in vacancy.get('skills_required', [])])
+    
+    if not required_skills:
+        return 70  # Если требования не указаны
+    
+    matching_skills = resume_skills.intersection(required_skills)
+    match_percentage = (len(matching_skills) / len(required_skills)) * 100
+    
+    return min(100, int(match_percentage))
+
+
+def _calculate_experience_score(parsed_resume: Dict, vacancy: Dict) -> int:
+    """Вычисляет оценку релевантности опыта"""
+    
+    years_experience = parsed_resume.get('total_years', 0)
+    required_level = vacancy.get('experience_level', 'middle')
+    
+    # Маппинг уровней на годы опыта
+    level_mapping = {
+        'junior': (0, 2),
+        'middle': (2, 5), 
+        'senior': (5, 10),
+        'lead': (8, 15)
+    }
+    
+    min_years, max_years = level_mapping.get(required_level, (2, 5))
+    
+    if years_experience < min_years:
+        # Недостаток опыта
+        return max(30, int(70 * (years_experience / min_years)))
+    elif years_experience > max_years:
+        # Переквалификация
+        return max(60, int(90 - (years_experience - max_years) * 5))
+    else:
+        # Подходящий опыт
+        return 90
+
+
+def _save_report_to_db(db, resume_id: int, report: Dict):
+    """Сохраняет отчет в базу данных в таблицу interview_reports"""
+    
+    try:
+        from app.models.interview import InterviewSession
+        from app.models.interview_report import InterviewReport, RecommendationType
+        
+        # Находим сессию интервью по resume_id
+        interview_session = db.query(InterviewSession).filter(
+            InterviewSession.resume_id == resume_id
+        ).first()
+        
+        if not interview_session:
+            logger.warning(f"[INTERVIEW_ANALYSIS] No interview session found for resume_id: {resume_id}")
+            return
+        
+        # Проверяем, есть ли уже отчет для этой сессии
+        existing_report = db.query(InterviewReport).filter(
+            InterviewReport.interview_session_id == interview_session.id
+        ).first()
+        
+        if existing_report:
+            logger.info(f"[INTERVIEW_ANALYSIS] Updating existing report for session: {interview_session.id}")
+            # Обновляем существующий отчет
+            _update_report_from_dict(existing_report, report)
+            existing_report.updated_at = datetime.utcnow()
+            db.add(existing_report)
+        else:
+            logger.info(f"[INTERVIEW_ANALYSIS] Creating new report for session: {interview_session.id}")
+            # Создаем новый отчет
+            new_report = _create_report_from_dict(interview_session.id, report)
+            db.add(new_report)
+        
+        logger.info(f"[INTERVIEW_ANALYSIS] Report saved for resume_id: {resume_id}, session: {interview_session.id}")
+        
+    except Exception as e:
+        logger.error(f"[INTERVIEW_ANALYSIS] Error saving report: {str(e)}")
+
+
+def _create_report_from_dict(interview_session_id: int, report: Dict) -> 'InterviewReport':
+    """Создает объект InterviewReport из словаря отчета"""
+    from app.models.interview_report import InterviewReport, RecommendationType
+    
+    # Извлекаем баллы по критериям
+    scores = report.get('scores', {})
+    
+    return InterviewReport(
+        interview_session_id=interview_session_id,
+        
+        # Основные критерии оценки
+        technical_skills_score=scores.get('technical_skills', {}).get('score', 0),
+        technical_skills_justification=scores.get('technical_skills', {}).get('justification', ''),
+        technical_skills_concerns=scores.get('technical_skills', {}).get('concerns', ''),
+        
+        experience_relevance_score=scores.get('experience_relevance', {}).get('score', 0),
+        experience_relevance_justification=scores.get('experience_relevance', {}).get('justification', ''),
+        experience_relevance_concerns=scores.get('experience_relevance', {}).get('concerns', ''),
+        
+        communication_score=scores.get('communication', {}).get('score', 0),
+        communication_justification=scores.get('communication', {}).get('justification', ''),
+        communication_concerns=scores.get('communication', {}).get('concerns', ''),
+        
+        problem_solving_score=scores.get('problem_solving', {}).get('score', 0),
+        problem_solving_justification=scores.get('problem_solving', {}).get('justification', ''),
+        problem_solving_concerns=scores.get('problem_solving', {}).get('concerns', ''),
+        
+        cultural_fit_score=scores.get('cultural_fit', {}).get('score', 0),
+        cultural_fit_justification=scores.get('cultural_fit', {}).get('justification', ''),
+        cultural_fit_concerns=scores.get('cultural_fit', {}).get('concerns', ''),
+        
+        # Агрегированные поля
+        overall_score=report.get('overall_score', 0),
+        recommendation=RecommendationType(report.get('recommendation', 'reject')),
+        
+        # Дополнительные поля
+        strengths=report.get('strengths', []),
+        weaknesses=report.get('weaknesses', []),
+        red_flags=report.get('red_flags', []),
+        
+        # Метрики интервью
+        dialogue_messages_count=report.get('analysis_context', {}).get('dialogue_messages_count', 0),
+        
+        # Дополнительная информация
+        next_steps=report.get('next_steps', ''),
+        questions_analysis=report.get('questions_analysis', []),
+        
+        # Метаданные анализа
+        analysis_method=report.get('analysis_method', 'openai_gpt4'),
+    )
+
+
+def _update_report_from_dict(existing_report, report: Dict):
+    """Обновляет существующий отчет данными из словаря"""
+    from app.models.interview_report import RecommendationType
+    
+    scores = report.get('scores', {})
+    
+    # Основные критерии оценки
+    if 'technical_skills' in scores:
+        existing_report.technical_skills_score = scores['technical_skills'].get('score', 0)
+        existing_report.technical_skills_justification = scores['technical_skills'].get('justification', '')
+        existing_report.technical_skills_concerns = scores['technical_skills'].get('concerns', '')
+    
+    if 'experience_relevance' in scores:
+        existing_report.experience_relevance_score = scores['experience_relevance'].get('score', 0)
+        existing_report.experience_relevance_justification = scores['experience_relevance'].get('justification', '')
+        existing_report.experience_relevance_concerns = scores['experience_relevance'].get('concerns', '')
+    
+    if 'communication' in scores:
+        existing_report.communication_score = scores['communication'].get('score', 0)
+        existing_report.communication_justification = scores['communication'].get('justification', '')
+        existing_report.communication_concerns = scores['communication'].get('concerns', '')
+    
+    if 'problem_solving' in scores:
+        existing_report.problem_solving_score = scores['problem_solving'].get('score', 0)
+        existing_report.problem_solving_justification = scores['problem_solving'].get('justification', '')
+        existing_report.problem_solving_concerns = scores['problem_solving'].get('concerns', '')
+    
+    if 'cultural_fit' in scores:
+        existing_report.cultural_fit_score = scores['cultural_fit'].get('score', 0)
+        existing_report.cultural_fit_justification = scores['cultural_fit'].get('justification', '')
+        existing_report.cultural_fit_concerns = scores['cultural_fit'].get('concerns', '')
+    
+    # Агрегированные поля
+    if 'overall_score' in report:
+        existing_report.overall_score = report['overall_score']
+    
+    if 'recommendation' in report:
+        existing_report.recommendation = RecommendationType(report['recommendation'])
+    
+    # Дополнительные поля
+    if 'strengths' in report:
+        existing_report.strengths = report['strengths']
+    
+    if 'weaknesses' in report:
+        existing_report.weaknesses = report['weaknesses']
+    
+    if 'red_flags' in report:
+        existing_report.red_flags = report['red_flags']
+    
+    # Метрики интервью
+    if 'analysis_context' in report:
+        existing_report.dialogue_messages_count = report['analysis_context'].get('dialogue_messages_count', 0)
+    
+    # Дополнительная информация
+    if 'next_steps' in report:
+        existing_report.next_steps = report['next_steps']
+    
+    if 'questions_analysis' in report:
+        existing_report.questions_analysis = report['questions_analysis']
+    
+    # Метаданные анализа
+    if 'analysis_method' in report:
+        existing_report.analysis_method = report['analysis_method']
+
+
+# Дополнительная задача для массового анализа
+@shared_task
+def analyze_multiple_candidates(resume_ids: List[int]):
+    """
+    Анализирует несколько кандидатов и возвращает их рейтинг
+    
+    Args:
+        resume_ids: Список ID резюме для анализа
+        
+    Returns:
+        List[Dict]: Список кандидатов с оценками, отсортированный по рейтингу
+    """
+    logger.info(f"[MASS_ANALYSIS] Starting analysis for {len(resume_ids)} candidates")
+    
+    results = []
+    
+    for resume_id in resume_ids:
+        try:
+            result = generate_interview_report(resume_id)
+            if 'error' not in result:
+                results.append({
+                    'resume_id': resume_id,
+                    'candidate_name': result.get('candidate_name', 'Unknown'),
+                    'overall_score': result.get('overall_score', 0),
+                    'recommendation': result.get('recommendation', 'reject'),
+                    'position': result.get('position', 'Unknown')
+                })
+        except Exception as e:
+            logger.error(f"[MASS_ANALYSIS] Error analyzing resume {resume_id}: {str(e)}")
+    
+    # Сортируем по общему баллу
+    results.sort(key=lambda x: x['overall_score'], reverse=True)
+    
+    logger.info(f"[MASS_ANALYSIS] Completed analysis for {len(results)} candidates")
+    return results

celery_worker/tasks.py

@@ -9,6 +9,9 @@ from celery_worker.database import get_sync_session, SyncResumeRepository
 from rag.llm.model import ResumeParser
 from rag.registry import registry
 
+# Импортируем новые задачи анализа интервью
+from celery_worker.interview_analysis_task import generate_interview_report, analyze_multiple_candidates
+
 
 def generate_interview_plan(resume_id: int, combined_data: Dict[str, Any]) -> Dict[str, Any]:
     """Генерирует план интервью на основе резюме и вакансии"""

main.py

@@ -5,6 +5,8 @@ from app.core.session_middleware import SessionMiddleware
 from app.routers import vacancy_router, resume_router
 from app.routers.session_router import router as session_router
 from app.routers.interview_router import router as interview_router
+from app.routers.analysis_router import router as analysis_router
+from app.routers.admin_router import router as admin_router
 
 
 @asynccontextmanager
@@ -34,6 +36,8 @@ app.include_router(vacancy_router, prefix="/api/v1")
 app.include_router(resume_router, prefix="/api/v1")
 app.include_router(session_router, prefix="/api/v1")
 app.include_router(interview_router, prefix="/api/v1")
+app.include_router(analysis_router, prefix="/api/v1")
+app.include_router(admin_router, prefix="/api/v1")
 
 
 @app.get("/")

migrations/versions/772538626a9e_revert_json_fields_back_to_json_type.py

@@ -0,0 +1,48 @@
+"""revert json fields back to json type
+
+Revision ID: 772538626a9e
+Revises: a816820baadb
+Create Date: 2025-09-04 00:02:15.230498
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '772538626a9e'
+down_revision: Union[str, Sequence[str], None] = 'a816820baadb'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # Convert TEXT fields back to JSON for proper dict handling
+    op.execute("""
+        ALTER TABLE resume 
+        ALTER COLUMN parsed_data TYPE JSON USING parsed_data::JSON,
+        ALTER COLUMN interview_plan TYPE JSON USING interview_plan::JSON
+    """)
+    
+    op.execute("""
+        ALTER TABLE interview_sessions 
+        ALTER COLUMN dialogue_history TYPE JSON USING dialogue_history::JSON
+    """)
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # Convert JSON fields back to TEXT
+    op.execute("""
+        ALTER TABLE resume 
+        ALTER COLUMN parsed_data TYPE TEXT USING parsed_data::TEXT,
+        ALTER COLUMN interview_plan TYPE TEXT USING interview_plan::TEXT
+    """)
+    
+    op.execute("""
+        ALTER TABLE interview_sessions 
+        ALTER COLUMN dialogue_history TYPE TEXT USING dialogue_history::TEXT
+    """)

migrations/versions/9c60c15f7846_add_interview_reports_table_with_.py

@@ -0,0 +1,99 @@
+"""add interview reports table with scoring fields
+
+Revision ID: 9c60c15f7846
+Revises: 772538626a9e
+Create Date: 2025-09-04 12:16:56.495018
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '9c60c15f7846'
+down_revision: Union[str, Sequence[str], None] = '772538626a9e'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Create interview reports table with scoring fields."""
+    
+    # Create enum type for recommendation
+    op.execute("CREATE TYPE recommendationtype AS ENUM ('STRONGLY_RECOMMEND', 'RECOMMEND', 'CONSIDER', 'REJECT')")
+    
+    # Create interview_reports table
+    op.execute("""
+    CREATE TABLE interview_reports (
+        id SERIAL PRIMARY KEY,
+        interview_session_id INTEGER NOT NULL UNIQUE,
+        
+        -- Core scoring criteria (0-100)
+        technical_skills_score INTEGER NOT NULL CHECK (technical_skills_score >= 0 AND technical_skills_score <= 100),
+        technical_skills_justification VARCHAR(1000),
+        technical_skills_concerns VARCHAR(500),
+        
+        experience_relevance_score INTEGER NOT NULL CHECK (experience_relevance_score >= 0 AND experience_relevance_score <= 100),
+        experience_relevance_justification VARCHAR(1000),
+        experience_relevance_concerns VARCHAR(500),
+        
+        communication_score INTEGER NOT NULL CHECK (communication_score >= 0 AND communication_score <= 100),
+        communication_justification VARCHAR(1000),
+        communication_concerns VARCHAR(500),
+        
+        problem_solving_score INTEGER NOT NULL CHECK (problem_solving_score >= 0 AND problem_solving_score <= 100),
+        problem_solving_justification VARCHAR(1000),
+        problem_solving_concerns VARCHAR(500),
+        
+        cultural_fit_score INTEGER NOT NULL CHECK (cultural_fit_score >= 0 AND cultural_fit_score <= 100),
+        cultural_fit_justification VARCHAR(1000),
+        cultural_fit_concerns VARCHAR(500),
+        
+        -- Aggregated fields
+        overall_score INTEGER NOT NULL CHECK (overall_score >= 0 AND overall_score <= 100),
+        recommendation recommendationtype NOT NULL,
+        
+        -- Analysis arrays
+        strengths JSON,
+        weaknesses JSON,
+        red_flags JSON,
+        
+        -- Interview metrics
+        questions_quality_score FLOAT CHECK (questions_quality_score >= 0 AND questions_quality_score <= 10),
+        interview_duration_minutes INTEGER CHECK (interview_duration_minutes >= 0),
+        response_count INTEGER CHECK (response_count >= 0),
+        dialogue_messages_count INTEGER CHECK (dialogue_messages_count >= 0),
+        
+        -- Additional info
+        next_steps VARCHAR(1000),
+        interviewer_notes TEXT,
+        questions_analysis JSON,
+        
+        -- Analysis metadata
+        analysis_method VARCHAR(50) DEFAULT 'openai_gpt4',
+        llm_model_used VARCHAR(100),
+        analysis_duration_seconds INTEGER CHECK (analysis_duration_seconds >= 0),
+        
+        -- Timestamps
+        created_at TIMESTAMP NOT NULL DEFAULT NOW(),
+        updated_at TIMESTAMP NOT NULL DEFAULT NOW(),
+        
+        -- Foreign key
+        FOREIGN KEY (interview_session_id) REFERENCES interview_sessions(id)
+    )
+    """)
+    
+    # Create useful indexes
+    op.execute("CREATE INDEX idx_interview_reports_overall_score ON interview_reports (overall_score DESC)")
+    op.execute("CREATE INDEX idx_interview_reports_recommendation ON interview_reports (recommendation)")
+    op.execute("CREATE INDEX idx_interview_reports_technical_skills ON interview_reports (technical_skills_score DESC)")
+    op.execute("CREATE INDEX idx_interview_reports_communication ON interview_reports (communication_score DESC)")
+    op.execute("CREATE INDEX idx_interview_reports_session_id ON interview_reports (interview_session_id)")
+
+
+def downgrade() -> None:
+    """Drop interview reports table."""
+    op.execute("DROP TABLE IF EXISTS interview_reports")
+    op.execute("DROP TYPE IF EXISTS recommendationtype")

migrations/versions/9d415bf0ff2e_add_dialogue_history_column_only.py

@@ -0,0 +1,45 @@
+"""add dialogue_history column only
+
+Revision ID: 9d415bf0ff2e
+Revises: 53d8b753cb71
+Create Date: 2025-09-03 18:04:49.726882
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '9d415bf0ff2e'
+down_revision: Union[str, Sequence[str], None] = 'c2d48b31ee30'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # Сначала создаем таблицу interview_sessions (если была удалена)
+    op.create_table('interview_sessions',
+    sa.Column('resume_id', sa.Integer(), nullable=False),
+    sa.Column('room_name', sa.String(length=255), nullable=False),
+    sa.Column('status', sa.Enum('created', 'active', 'completed', 'failed', name='interviewstatus', create_type=False), nullable=True),
+    sa.Column('transcript', sa.Text(), nullable=True),
+    sa.Column('ai_feedback', sa.Text(), nullable=True),
+    sa.Column('dialogue_history', sa.JSON(), nullable=True),
+    sa.Column('ai_agent_pid', sa.Integer(), nullable=True),
+    sa.Column('ai_agent_status', sa.String(), nullable=False),
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('started_at', sa.DateTime(), nullable=False),
+    sa.Column('completed_at', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['resume_id'], ['resume.id'], ),
+    sa.PrimaryKeyConstraint('id'),
+    sa.UniqueConstraint('room_name')
+    )
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # Удаляем всю таблицу
+    op.drop_table('interview_sessions')

migrations/versions/a816820baadb_change_json_fields_to_text_for_proper_.py

@@ -0,0 +1,59 @@
+"""change json fields to text for proper utf8
+
+Revision ID: a816820baadb
+Revises: c9bcdd2ddeeb
+Create Date: 2025-09-03 23:45:13.221735
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'a816820baadb'
+down_revision: Union[str, Sequence[str], None] = 'c9bcdd2ddeeb'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Change JSON fields to TEXT for proper UTF-8 handling."""
+    # Convert JSON fields to TEXT for proper UTF-8 storage
+    op.execute("""
+        ALTER TABLE resume 
+        ALTER COLUMN parsed_data TYPE TEXT USING parsed_data::TEXT,
+        ALTER COLUMN interview_plan TYPE TEXT USING interview_plan::TEXT
+    """)
+    
+    op.execute("""
+        ALTER TABLE interview_sessions 
+        ALTER COLUMN dialogue_history TYPE TEXT USING dialogue_history::TEXT
+    """)
+    
+    # Also fix status column
+    op.alter_column('interview_sessions', 'status',
+               existing_type=sa.VARCHAR(length=50),
+               nullable=False,
+               existing_server_default=sa.text("'created'::character varying"))
+
+
+def downgrade() -> None:
+    """Convert TEXT fields back to JSON."""
+    # Convert TEXT fields back to JSON
+    op.execute("""
+        ALTER TABLE resume 
+        ALTER COLUMN parsed_data TYPE JSON USING parsed_data::JSON,
+        ALTER COLUMN interview_plan TYPE JSON USING interview_plan::JSON
+    """)
+    
+    op.execute("""
+        ALTER TABLE interview_sessions 
+        ALTER COLUMN dialogue_history TYPE JSON USING dialogue_history::JSON
+    """)
+    
+    op.alter_column('interview_sessions', 'status',
+               existing_type=sa.VARCHAR(length=50),
+               nullable=True,
+               existing_server_default=sa.text("'created'::character varying"))

migrations/versions/c2d48b31ee30_add_interview_dialogue_and_analysis_.py

@@ -0,0 +1,48 @@
+"""add interview dialogue and analysis tables
+
+Revision ID: c2d48b31ee30
+Revises: de11b016b35a
+Create Date: 2025-09-03 17:55:41.653125
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = 'c2d48b31ee30'
+down_revision: Union[str, Sequence[str], None] = 'de11b016b35a'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index(op.f('ix_interview_sessions_id'), table_name='interview_sessions')
+    op.drop_table('interview_sessions')
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('interview_sessions',
+    sa.Column('id', sa.INTEGER(), autoincrement=True, nullable=False),
+    sa.Column('resume_id', sa.INTEGER(), autoincrement=False, nullable=False),
+    sa.Column('room_name', sa.VARCHAR(length=255), autoincrement=False, nullable=False),
+    sa.Column('status', postgresql.ENUM('created', 'active', 'completed', 'failed', name='interviewstatus'), autoincrement=False, nullable=False),
+    sa.Column('transcript', sa.TEXT(), autoincrement=False, nullable=True),
+    sa.Column('ai_feedback', sa.TEXT(), autoincrement=False, nullable=True),
+    sa.Column('started_at', postgresql.TIMESTAMP(), autoincrement=False, nullable=False),
+    sa.Column('completed_at', postgresql.TIMESTAMP(), autoincrement=False, nullable=True),
+    sa.Column('ai_agent_pid', sa.INTEGER(), autoincrement=False, nullable=True),
+    sa.Column('ai_agent_status', sa.VARCHAR(), server_default=sa.text("'not_started'::character varying"), autoincrement=False, nullable=False),
+    sa.ForeignKeyConstraint(['resume_id'], ['resume.id'], name=op.f('interview_sessions_resume_id_fkey')),
+    sa.PrimaryKeyConstraint('id', name=op.f('interview_sessions_pkey')),
+    sa.UniqueConstraint('room_name', name=op.f('interview_sessions_room_name_key'), postgresql_include=[], postgresql_nulls_not_distinct=False)
+    )
+    op.create_index(op.f('ix_interview_sessions_id'), 'interview_sessions', ['id'], unique=False)
+    # ### end Alembic commands ###

migrations/versions/c9bcdd2ddeeb_recreate_interview_sessions_with_.py

@@ -0,0 +1,46 @@
+"""recreate interview_sessions with varchar status
+
+Revision ID: c9bcdd2ddeeb
+Revises: 9d415bf0ff2e
+Create Date: 2025-09-03 18:07:59.433986
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'c9bcdd2ddeeb'
+down_revision: Union[str, Sequence[str], None] = '9d415bf0ff2e'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema.""" 
+    # Создаем таблицу interview_sessions заново
+    op.execute("DROP TABLE IF EXISTS interview_sessions CASCADE")
+    
+    op.create_table('interview_sessions',
+    sa.Column('id', sa.Integer(), nullable=False),
+    sa.Column('resume_id', sa.Integer(), nullable=False),
+    sa.Column('room_name', sa.String(length=255), nullable=False),
+    sa.Column('status', sa.String(50), nullable=True, server_default='created'),
+    sa.Column('transcript', sa.Text(), nullable=True),
+    sa.Column('ai_feedback', sa.Text(), nullable=True),
+    sa.Column('dialogue_history', sa.JSON(), nullable=True),
+    sa.Column('ai_agent_pid', sa.Integer(), nullable=True),
+    sa.Column('ai_agent_status', sa.String(50), nullable=False, server_default='not_started'),
+    sa.Column('started_at', sa.DateTime(), nullable=False, server_default=sa.text('CURRENT_TIMESTAMP')),
+    sa.Column('completed_at', sa.DateTime(), nullable=True),
+    sa.ForeignKeyConstraint(['resume_id'], ['resume.id'], ),
+    sa.PrimaryKeyConstraint('id'),
+    sa.UniqueConstraint('room_name')
+    )
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    op.drop_table('interview_sessions')